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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1512.06473 | 57 | Top-1 Err. â 0.22% 0.45% 0.21% 0.35% 0.36% 0.43% 0.29% 0.56% Compression 14.37Ã 20.15Ã 15.79Ã 19.66Ã 14.37Ã 20.15Ã 15.79Ã 19.66Ã Top-5 Err. â 0.07% 0.22% 0.11% 0.27% 0.14% 0.24% 0.11% 0.27% Method Q-CNN Q-CNN (EC)
# Appendix B: Optimization in Section 3.3.2
Assume we have N images to learn the quantization of a convolutional layer. For image In, we denote its input fea- ture maps as Sn â RdsÃdsÃCs and response feature maps as Tn â RdtÃdtÃCt, where ds, dt are the spatial sizes and Cs, Ct are the number of feature map channels. We use ps and pt to denote the spatial location in the input and re- sponse feature maps. The spatial location in the convolu- tional kernels is denoted as pk.
To learn quantization with error correction for the con- volutional layer, we attempt to optimize: | 1512.06473#57 | Quantized Convolutional Neural Networks for Mobile Devices | Recently, convolutional neural networks (CNN) have demonstrated impressive
performance in various computer vision tasks. However, high performance
hardware is typically indispensable for the application of CNN models due to
the high computation complexity, which prohibits their further extensions. In
this paper, we propose an efficient framework, namely Quantized CNN, to
simultaneously speed-up the computation and reduce the storage and memory
overhead of CNN models. Both filter kernels in convolutional layers and
weighting matrices in fully-connected layers are quantized, aiming at
minimizing the estimation error of each layer's response. Extensive experiments
on the ILSVRC-12 benchmark demonstrate 4~6x speed-up and 15~20x compression
with merely one percentage loss of classification accuracy. With our quantized
CNN model, even mobile devices can accurately classify images within one
second. | http://arxiv.org/pdf/1512.06473 | Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, Jian Cheng | cs.CV | Accepted by the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 2016 | null | cs.CV | 20151221 | 20160516 | [] |
1512.06473 | 58 | To learn quantization with error correction for the con- volutional layer, we attempt to optimize:
min OS DW BYP) PSLP. - Tare {DOM} {BE} nape (Peps) ⢠Pp where Dââ is the m-th sub-codebook, and Be) is the cor- responding sub-codeword assignment indicator for the con- volutional kernels at spatial location p,.
Similar to the fully-connected layer, we adopt a block co- ordinate descent approach to solve this optimization prob- lem. For the m-th subspace, we ï¬rstly deï¬ne its residual feature map as:
R(m) n,pt = Tn,pt â X (pk,ps) X mâ²6=m (D(mâ² )B(mâ² pk ) )T S(mâ² ) n,ps (15)
and then the optimization in the m-th subspace can be re2
formulated as:
2 min So SD (WBE) Ps â Row mâ¢m © oes Pt DO) {BIL} np (paps) P (16) Update D(â¢). With the assignment indicator {Ber}
ï¬xed, we let:
Lk,pk = {ct|B(m) pk (k, ct) = 1} (17) | 1512.06473#58 | Quantized Convolutional Neural Networks for Mobile Devices | Recently, convolutional neural networks (CNN) have demonstrated impressive
performance in various computer vision tasks. However, high performance
hardware is typically indispensable for the application of CNN models due to
the high computation complexity, which prohibits their further extensions. In
this paper, we propose an efficient framework, namely Quantized CNN, to
simultaneously speed-up the computation and reduce the storage and memory
overhead of CNN models. Both filter kernels in convolutional layers and
weighting matrices in fully-connected layers are quantized, aiming at
minimizing the estimation error of each layer's response. Extensive experiments
on the ILSVRC-12 benchmark demonstrate 4~6x speed-up and 15~20x compression
with merely one percentage loss of classification accuracy. With our quantized
CNN model, even mobile devices can accurately classify images within one
second. | http://arxiv.org/pdf/1512.06473 | Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, Jian Cheng | cs.CV | Accepted by the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 2016 | null | cs.CV | 20151221 | 20160516 | [] |
1512.06473 | 59 | ï¬xed, we let:
Lk,pk = {ct|B(m) pk (k, ct) = 1} (17)
We greedily update each sub-codeword in the m-th sub- codebook D(m) in a sequential style. For the k-th sub- codeword, we compute the corresponding residual feature map as:
Q(m) n,pt,k(ct) = R(m) n,pt (ct) â X (pk ,ps) X kâ²6=k X ctâLkâ²,pk D(m)T kâ² S(m) n,ps
and then we can alternatively optimize:
2 . (m)⢠@(m) _ Elm) min D7, 0 De Sein. â Ont. x (Ce) ke n,pt || (pe Ps) CEL p,, FP which can be transformed into a least square problem. By solving it, we can update the k-th sub-codeword.
pk }. We greedily update the sub-codeword assignment at each spatial location in the convolutional ker- nels in a sequential style. For the spatial location pk, we compute the corresponding residual feature map as: | 1512.06473#59 | Quantized Convolutional Neural Networks for Mobile Devices | Recently, convolutional neural networks (CNN) have demonstrated impressive
performance in various computer vision tasks. However, high performance
hardware is typically indispensable for the application of CNN models due to
the high computation complexity, which prohibits their further extensions. In
this paper, we propose an efficient framework, namely Quantized CNN, to
simultaneously speed-up the computation and reduce the storage and memory
overhead of CNN models. Both filter kernels in convolutional layers and
weighting matrices in fully-connected layers are quantized, aiming at
minimizing the estimation error of each layer's response. Extensive experiments
on the ILSVRC-12 benchmark demonstrate 4~6x speed-up and 15~20x compression
with merely one percentage loss of classification accuracy. With our quantized
CNN model, even mobile devices can accurately classify images within one
second. | http://arxiv.org/pdf/1512.06473 | Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, Jian Cheng | cs.CV | Accepted by the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 2016 | null | cs.CV | 20151221 | 20160516 | [] |
1512.05742 | 0 | 7 1 0 2
r a M 1 2 ] L C . s c [
3 v 2 4 7 5 0 . 2 1 5 1 : v i X r a
# A Survey of Available Corpora for Building Data-Driven Dialogue Systems
Iulian Vlad Serban DIRO, Universit´e de Montr´eal 2920 chemin de la Tour, Montr´eal, QC H3C 3J7, Canada
{IULIAN.VLAD.SERBAN} AT UMONTREAL DOT CA
Ryan Lowe Department of Computer Science, McGill University 3480 University st, Montr´eal, QC H3A 0E9, Canada
{RYAN.LOWE} AT MAIL DOT MCGILL DOT CA
Peter Henderson Department of Computer Science, McGill University 3480 University st, Montr´eal, QC H3A 0E9, Canada
{PETER.HENDERSON} AT MAIL DOT MCGILL DOT CA
Laurent Charlin Department of Computer Science, McGill University 3480 University st, Montr´eal, QC H3A 0E9, Canada
{LCHARLIN} AT CS DOT MCGILL DOT CA
Joelle Pineau Department of Computer Science, McGill University 3480 University st, Montr´eal, QC H3A 0E9, Canada
{JPINEAU} AT CS DOT MCGILL DOT CA
Editor: David Traum
# Abstract | 1512.05742#0 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 1 | {JPINEAU} AT CS DOT MCGILL DOT CA
Editor: David Traum
# Abstract
During the past decade, several areas of speech and language understanding have witnessed sub- stantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through signiï¬cant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are fea- sible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss im- portant characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.
# 1. Introduction
Dialogue systems, also known as interactive conversational agents, virtual agents or sometimes chatterbots, are useful in a wide range of applications ranging from technical support services to language learning tools and entertainment (Young et al., 2013; Shawar and Atwell, 2007b). Large1 | 1512.05742#1 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
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1512.05742 | 2 | scale data-driven methods, which use recorded data to automatically infer knowledge and strategies, are becoming increasingly important in speech and language understanding and generation. Speech recognition performance has increased tremendously over the last decade due to innovations in deep learning architectures (Hinton et al., 2012; Goodfellow et al., 2015). Similarly, a wide range of data- driven machine learning methods have been shown to be effective for natural language processing, including tasks relevant to dialogue, such as dialogue act classiï¬cation (Reithinger and Klesen, 1997; Stolcke et al., 2000), dialogue state tracking (Thomson and Young, 2010; Wang and Lemon, 2013; Ren et al., 2013; Henderson et al., 2013; Williams et al., 2013; Henderson et al., 2014c; Kim et al., 2015), natural language generation (Langkilde and Knight, 1998; Oh and Rudnicky, 2000; Walker et al., 2002; Ratnaparkhi, 2002; Stent et al., 2004; Rieser and Lemon, 2010; Mairesse et al., 2010; Mairesse and Young, 2014; Wen et al., 2015; Sharma et al., 2016), and dialogue policy learning (Young et al., 2013). We hypothesize | 1512.05742#2 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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"id": "1611.09268"
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"id": "1510.03055"
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1512.05742 | 3 | 2010; Mairesse and Young, 2014; Wen et al., 2015; Sharma et al., 2016), and dialogue policy learning (Young et al., 2013). We hypothesize that, in general, much of the recent progress is due to the availability of large public datasets, increased computing power, and new machine learning models, such as neural network architectures. To facilitate further research on building data-driven dialogue systems, this paper presents a broad survey of available dialogue corpora. | 1512.05742#3 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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1512.05742 | 4 | Corpus-based learning is not the only approach to training dialogue systems. Researchers have also proposed training dialogue systems online through live interaction with humans, and ofï¬ine using user simulator models and reinforcement learning methods (Levin et al., 1997; Georgila et al., 2006; Paek, 2006; Schatzmann et al., 2007; Jung et al., 2009; Schatzmann and Young, 2009; GaËsi´c et al., 2010, 2011; Daubigney et al., 2012; GaËsi´c et al., 2012; Su et al., 2013; Gasic et al., 2013; Pietquin and Hastie, 2013; Young et al., 2013; Mohan and Laird, 2014; Su et al., 2015; Piot et al., 2015; Cuay´ahuitl et al., 2015; Hiraoka et al., 2016; Fatemi et al., 2016; Asri et al., 2016; Williams and Zweig, 2016; Su et al., 2016). However, these approaches are beyond the scope of this survey. | 1512.05742#4 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
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1512.05742 | 5 | This survey is structured as follows. In the next section, we give a high-level overview of di- alogue systems. We brieï¬y discuss the purpose and goal of dialogue systems. Then we describe the individual system components that are relevant for data-driven approaches as well as holistic end-to-end dialogue systems. In Section 3, we discuss types of dialogue interactions and aspects relevant to building data-driven dialogue systems, from a corpus perspective, as well as modalities recorded in each corpus (e.g. text, speech and video). We further discuss corpora constructed from both human-human and human-machine interactions, corpora constructed using natural versus un- natural or constrained settings, and corpora constructed using works of ï¬ction. In Section 4, we present our survey over dialogue corpora according to the categories laid out in Sections 2-3. In particular, we categorize the corpora based on whether dialogues are between humans or between a human and a machine, and whether the dialogues are in written or spoken language. We discuss each corpus in turn while emphasizing how the dialogues were generated and collected, the topic of the dialogues, and the size of the | 1512.05742#5 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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1512.05742 | 6 | spoken language. We discuss each corpus in turn while emphasizing how the dialogues were generated and collected, the topic of the dialogues, and the size of the entire corpus. In Section 5, we discuss issues related to: cor- pus size, transfer learning between corpora, incorporation of external knowledge into the dialogue system, data-driven learning for contextualization and personalization, and automatic evaluation metrics. We conclude the survey in Section 6. | 1512.05742#6 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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1512.05742 | 7 | # 2. Characteristics of Data-Driven Dialogue Systems
This section offers a broad characterization of data-driven dialogue systems, which structures our presentation of the datasets.
2
# 2.1 An Overview of Dialogue Systems
The standard architecture for dialogue systems, shown in Figure 1, incorporates a Speech Rec- ognizer, Language Interpreter, State Tracker, Response Generator, Natural Language Generator, and Speech Synthesizer. In the case of text-based (written) dialogues, the Speech Recognizer and Speech Synthesizer can be left out. While some of the literature on dialogue systems identiï¬es only the State Tracker and Response Selection components as belonging inside the dialogue man- ager (Young, 2000), throughout this paper we adopt a broader view where language understanding and generation are incorporated within the dialogue system. This leaves space for the development and analysis of end-to-end dialogue systems (Ritter et al., 2011; Vinyals and Le, 2015; Lowe et al., 2015a; Sordoni et al., 2015b; Shang et al., 2015; Li et al., 2015; Serban et al., 2016; Serban et al., 2017b,a; Dodge et al., 2015; Williams and Zweig, 2016; Weston, 2016). | 1512.05742#7 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 8 | We focus on corpus-based data-driven dialogue systems. That is, systems composed of machine learning solutions using corpora constructed from real-world data. These system components have variables or parameters that are optimized based on statistics observed in dialogue corpora. In particular, we focus on systems where the majority of variables and parameters are optimized. Such corpus-based data-driven systems should be contrasted to systems where each component is hand- crafted by engineers â for example, components deï¬ned by an a priori ï¬xed set of deterministic rules (e.g. Weizenbaum (1966); McGlashan et al. (1992)). These systems should also be contrasted with systems learning online, such as when the free variables and parameters are optimized directly based on interactions with humans (e.g. GaËsi´c et al. (2011)). Still, it is worth noting that it is possible to combine different types of learning within one system. For example, some parameters may be learned using statistics observed in a corpus, while other parameters may be learned through interactions with humans. | 1512.05742#8 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
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"id": "1606.03632"
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1512.05742 | 9 | While there are substantial opportunities to improve each of the components in Figure 1 through (corpus-based) data-driven approaches, within this survey we focus primarily on datasets suitable to enhance the components inside the Dialogue System box. It is worth noting that the Natural Language Interpreter and Generator are core problems in Natural Language Processing with appli- cations well beyond dialogue systems.
Automatic Speech Natural Language Dialogue State Recognizer Interpreter Tracker Text-To-Speech Natural Language Dialogue a P Response Synthesizer Generator fi Selection Dialogue System
Figure 1: Dialogue System Diagram
3
# 2.2 Tasks and objectives
Dialogue systems have been built for a wide range of purposes. A useful distinction can be made between goal-driven dialogue systems, such as technical support services, and non-goal-driven dia- logue systems, such as language learning tools or computer game characters. Although both types of systems do in fact have objectives, typically the goal-driven dialogue systems have a well-deï¬ned measure of performance that is explicitly related to task completion. | 1512.05742#9 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
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"id": "1606.03632"
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1512.05742 | 10 | Non-goal-driven Dialogue Systems. Research on non-goal-driven dialogue systems goes back to the mid-60s. It began, perhaps, with Weizenbaumâs famous program ELIZA, a system based only on simple text parsing rules that managed to convincingly mimic a Rogerian psychotherapist by persistently rephrasing statements or asking questions (Weizenbaum, 1966). This line of research was continued by Colby (1981), who used simple text parsing rules to construct the dialogue system PARRY, which managed to mimic the pathological behaviour of a paranoid patient to the extent that clinicians could not distinguish it from real patients. However, neither of these two systems used data-driven learning approaches. Later work, such as the MegaHal system by Hutchens and Alder (1998), started to apply data-driven methods (Shawar and Atwell, 2007b). Hutchens and Alder (1998) proposed modelling dialogue as a stochastic sequence of discrete symbols (words) using 4âth order Markov chains. Given a user utterance, their system generated a response by following a two-step procedure: ï¬rst, a sequence of topic keywords, used to create a seed reply, was | 1512.05742#10 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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1512.05742 | 11 | utterance, their system generated a response by following a two-step procedure: ï¬rst, a sequence of topic keywords, used to create a seed reply, was ex- tracted from the userâs utterance; second, starting from the seed reply, two separate Markov chains generated the words preceding and proceeding the seed keywords. This procedure produced many candidate responses, from which the highest entropy response was returned to the user. Under the assumption that the coverage of different topics and general ï¬uency is of primary importance, the 4âth order Markov chains were trained on a mixture of data sources ranging from real and ï¬ctive dialogues to arbitrary texts. Unfortunately, until very recently, such data-driven dialogue systems were not applied widely in real-world applications (Perez-Marin and Pascual-Nieto, 2011; Shawar and Atwell, 2007b). Part of the reason for this might be due to their non-goal-driven nature, which made them hard to commercialize. Another barrier to commercialization might have been the lack of theoretical and empirical understanding of such systems. Nevertheless, in a similar spirit over the past few years, neural network architectures trained on | 1512.05742#11 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
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1512.05742 | 12 | have been the lack of theoretical and empirical understanding of such systems. Nevertheless, in a similar spirit over the past few years, neural network architectures trained on large-scale corpora have been investigated. These models have demonstrated promising results for several non-goal-driven dialogue tasks (Rit- ter et al., 2011; Vinyals and Le, 2015; Lowe et al., 2015a; Sordoni et al., 2015b; Shang et al., 2015; Li et al., 2015; Serban et al., 2016; Serban et al., 2017b,a; Dodge et al., 2015; Williams and Zweig, 2016; Weston, 2016). However, they require having sufï¬ciently large corpora â in the hundreds of millions or even billions of words â in order to achieve these results. | 1512.05742#12 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
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"id": "1606.03632"
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1512.05742 | 13 | Goal-driven Dialogue Systems. Initial work on goal-driven dialogue systems was primarily based on deterministic hand-crafted rules coupled with learned speech recognition models (e.g. off- the-shelf speech recognition software). One example is the SUNDIAL project, which was capable of providing timetable information about trains and airplanes, as well as taking airplane reserva- tions (Aust et al., 1995; McGlashan et al., 1992; Simpson and Eraser, 1993). Later, machine learn- ing techniques were used to classify the intention (or need) of the user, as well as to bridge the gap between text and speech (e.g. by taking into account uncertainty related to the outputs of the speech recognition model) (Gorin et al., 1997). Research in this area started to take off during the mid 1990s, when researchers began to formulate dialogue as a sequential decision making problem based on Markov decision processes (Singh et al., 1999; Young et al., 2013; Paek, 2006; Pieraccini
4 | 1512.05742#13 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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1512.05742 | 14 | 4
et al., 2009). Unlike non-goal-driven systems, industry played a major role and enabled researchers to have access to (at the time) relatively large dialogue corpora for certain tasks, such as recordings from technical support call centres. Although research in the past decade has continued to push the ï¬eld towards data-driven approaches, commercial systems are highly domain-speciï¬c and heavily based on hand-crafted rules and features (Young et al., 2013). In particular, many of the tasks and datasets available are constrained to narrow domains.
# 2.3 Learning Dialogue System Components | 1512.05742#14 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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"id": "1611.09268"
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1512.05742 | 15 | # 2.3 Learning Dialogue System Components
Modern dialogue systems consist of several components, as illustrated in Figure 1. Several of the dialogue system components can be learned through so-called discriminative models, which aim to predict labels or annotations relevant to other parts of the dialogue system. Discriminative models fall into the machine learning paradigm of supervised learning. When the labels of interest are discrete, the models are called classiï¬cation models, which is the most common case. When the labels of interest are continuous, the models are called regression models. One popular approach for tackling the discriminative task is to learn a probabilistic model of the labels conditioned on the available information P (Y |X), where Y is the label of interest (e.g. a discrete variable representing the user intent) and X is the available information (e.g. utterances in the conversation). Another popular approach is to use maximum margin classiï¬ers, such as support vector machines (Cristianini and Shawe-Taylor, 2000).
Although it is beyond the scope of this paper to provide a survey over such system components, we now give a brief example of each component. This will motivate and facilitate the dataset analysis. | 1512.05742#15 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
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"id": "1510.03055"
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1512.05742 | 16 | Although it is beyond the scope of this paper to provide a survey over such system components, we now give a brief example of each component. This will motivate and facilitate the dataset analysis.
Natural Language Interpreter. An example of a discriminative model is the user intent clas- siï¬cation model, which acts as the Natural Language Interpreter. This model is trained to predict the intent of a user conditioned on the utterances of that user. In this case, the intent is called the label (or target or output), and the conditioned utterances are called the conditioning variables (or inputs). Training this model requires examples of pairs of user utterances and intentions. One way to obtain these example pairs would be to ï¬rst record written dialogues between humans carrying out a task, and then to have humans annotate each utterance with its intention label. Depending on the complexity of the domain, this may require training the human annotators to reach a certain level of agreement between annotators. | 1512.05742#16 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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1512.05742 | 17 | Dialogue State Tracker. A Dialogue State Tracker might similarly be implemented as a classi- ï¬cation model (Williams et al., 2013). At any given point in the dialogue, such a model will take as input all the user utterances and user intention labels estimated by a Natural Language Interpreter model so far and output a distribution over possible dialogue states. One common way to represent dialogue states are through slot-value pairs. For example, a dialogue system providing timetable information for trains might have three different slots: departure city, arrival city, and departure time. Each slot may take one of several discrete values (e.g. departure city could take values from a list of city names). The task of the Dialogue State Tracker is then to output a distribution over every possible combination of slot-value pairs. This distribution â or alternatively, the K dialogue states with the highest probability â may then be used by other parts of the dialogue system. The Dialogue State Tracker model can be trained on examples of dialogue utterances and dialogue states labelled by humans.
5 | 1512.05742#17 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
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"id": "1611.09268"
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"id": "1510.03055"
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"id": "1607.00070"
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"id": "1503.02364"
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"id": "1511.08099"
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"id": "1604.06045"
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"id": "1606.01269"
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{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 18 | 5
Dialogue Response Selection. Given the dialogue state distribution provided by the Dialogue State Tracker, the Dialogue Response Selection component must select the correct system response (or action). This component may also be implemented as a classiï¬cation model that maps dialogue states to a probability over a discrete set of responses. For example, in a dialogue system provid- ing timetable information for trains, the set of responses might include providing information (e.g. providing the departure time of the next train with a speciï¬c departure and arrival city) and clariï¬- cation questions (e.g. asking the user to re-state their departure city). The model may be trained on example pairs of dialogue states and responses. | 1512.05742#18 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
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{
"id": "1606.02689"
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{
"id": "1606.01269"
},
{
"id": "1605.03481"
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{
"id": "1506.05869"
}
] |
1512.05742 | 19 | Natural Language Generator. Given a dialogue system response (e.g. a response providing the departure time of a train), the Natural Language Generator must output the natural language utterance of the system. This has often been implemented in commercial goal-driven dialogue systems using hand-crafted rules. Another option is to learn a discriminative model to select a natural language response. In this case, the output space may be deï¬ned as a set of so-called surface form sentences (e.g. âThe requested train leaves city X at time Yâ, where X and Y are placeholder values). Given the system response, the classiï¬cation model must choose an appropriate surface form. Afterwards, the chosen surface form will have the placeholder values substituted in appropriately (e.g. X will be replaced by the appropriate city name through a database look up). As with other classiï¬cation models, this model may be trained on example pairs of system responses and surface forms. | 1512.05742#19 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 20 | Discriminative models have allowed goal-driven dialogue systems to make signiï¬cant progress (Williams et al., 2013). With proper annotations, discriminative models can be evaluated automat- ically and accurately. Furthermore, once trained on a given dataset, these models may be plugged into a fully-deployed dialogue system (e.g. a classiï¬cation model for user intents may be used as input to a dialogue state tracker).
# 2.4 End-to-end Dialogue Systems | 1512.05742#20 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 21 | Not all dialogue systems conform to the architecture shown in Figure 1. In particular, so-called end-to-end dialogue system architectures based on neural networks have shown promising results on several dialogue tasks (Ritter et al., 2011; Vinyals and Le, 2015; Lowe et al., 2015a; Sordoni et al., 2015b; Shang et al., 2015; Li et al., 2015; Serban et al., 2016; Serban et al., 2017b,a; Dodge et al., 2015). In their purest form, these models take as input a dialogue in text form and output a response (or a distribution over responses). We call these systems end-to-end dialogue systems because they possess two important properties. First, they do not contain or require learning any sub-components (such as Natural Language Interpreters or Dialogue State Trackers). Consequently, there is no need to collect intermediate labels (e.g. user intention or dialogue state labels). Second, all model parameters are optimized w.r.t. a single objective function. Often the objective function chosen is maximum log-likelihood (or cross-entropy) on a ï¬xed corpus of dialogues. Although in the original formulation these models depended only on the dialogue context, they may be extended to also depend on outputs from other components (e.g. outputs from the speech recognition tracker), and on external knowledge (e.g. external databases). | 1512.05742#21 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
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{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 22 | End-to-end dialogue systems can be divided into two categories: those that select deterministi- cally from a ï¬xed set of possible responses, and those that attempt to generate responses by keeping a posterior distribution over possible utterances. Systems in the ï¬rst category map the dialogue his- tory, tracker outputs and external knowledge (e.g. a database, which can be queried by the system)
6
# to a response action:
fθ : {dialogue history, tracker outputs, external knowledge} â action at, | 1512.05742#22 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 23 | where at is the dialogue system response action at time t, and θ is the set of parameters that deï¬nes f . Information retrieval and ranking-based systems â systems that search through a database of dialogues and pick responses with the most similar context, such as the model proposed by Banchs and Li (2012) â belong to this category. In this case, the mapping function fθ projects the dialogue history into a Euclidean space (e.g. using TF-IDF bag-of-words representations). The response is then found by projecting all potential responses into the same Euclidean space, and the response closest to the desirable response region is selected. The neural network proposed by Lowe et al. (2015a) also belongs to this category. In this case, the dialogue history is projected into a Euclidean space using a recurrent neural network encoding the dialogue word-by-word. Similarly, a set of can- didate responses are mapped into the same Euclidean space using another recurrent neural network encoding the response word-by-word. Finally, a relevance score is computed between the dialogue context and each candidate response, and the response with the highest score is returned. Hybrid or combined models, | 1512.05742#23 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 24 | Finally, a relevance score is computed between the dialogue context and each candidate response, and the response with the highest score is returned. Hybrid or combined models, such as the model built on both a phrase-based statistical machine translation system and a recurrent neural network proposed by Sordoni et al. (2015b), also belong to this cate- gory. In this case, a response is generated by deterministically creating a ï¬xed number of answers using the machine translation system and then picking the response according to the score given by a a neural network. Although both of its sub-components are based on probabilistic models, the ï¬nal model does not construct a probability distribution over all possible responses.1 | 1512.05742#24 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
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{
"id": "1606.02689"
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{
"id": "1606.01269"
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{
"id": "1605.03481"
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{
"id": "1506.05869"
}
] |
1512.05742 | 26 | Systems based on generative recurrent neural networks belong to this category (Vinyals and Le, 2015). By breaking down eq. (2) into a product of probabilities over words, responses can be generated by sampling word-by-word from their probability distribution. Unlike the deterministic response models, these systems are also able to generate entirely novel responses (e.g. by sampling word-by-word). Highly probable responses, i.e. the response with the highest probability, can fur- ther be generated by using a method known as beam-search (Graves, 2012). These systems project each word into a Euclidean space (known as a word embedding) (Bengio et al., 2003); they also project the dialogue history and external knowledge into a Euclidean space (Wen et al., 2015; Lowe et al., 2015b). Similarly, the system proposed by Ritter et al. (2011) belongs to this category. Their model uses a statistical machine translation model to map a dialogue history to its response. When trained solely on text, these generative models can be viewed as unsupervised learning models, because they aim to reproduce data distributions. In other words, the models learn to assign a prob- ability to every possible conversation, and since they generate responses word by word, they must learn to simulate the behaviour of the agents in the training corpus. | 1512.05742#26 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 27 | Early reinforcement learning dialogue systems with stochastic policies also belong to this cat- egory (the NJFun system (Singh et al., 2002) is an example of this). In contrast to the neural network and statistical machine translation systems, these reinforcement learning systems typically
1. Although the model does not require intermediate labels, it consists of sub-components whose parameters are trained with different objective functions. Therefore, strictly speaking, this is not an end-to-end model.
7
(1)
(2)
have very small sets of possible hand-crafted system states (e.g. hand-crafted features describing the dialogue state). The action space is also limited to a small set of pre-deï¬ned responses. This makes it possible to apply established reinforcement learning algorithms to train them either online or ofï¬ine, however it also severely limits their application area. As Singh et al. (Singh et al., 2002, p.5) remark: âWe view the design of an appropriate state space as application-dependent, and a task for a skilled system designer.â
# 3. Dialogue Interaction Types & Aspects
This section provides a high-level discussion of different types of dialogue interactions and their salient aspects. The categorization of dialogues is useful for understanding the utility of various datasets for particular applications, as well as for grouping these datasets together to demonstrate available corpora in a given area. | 1512.05742#27 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 28 | # 3.1 Written, Spoken & Multi-modal Corpora
An important distinction between dialogue corpora is whether participants (interlocutors) interact through written language, spoken language, or in a multi-modal setting (e.g. using both speech and visual modalities). Written and spoken language differ substantially w.r.t. their linguistic properties. .Spoken language tends to be less formal, containing lower information content and many more pronouns than written language (Carter and McCarthy, 2006; Biber and Finegan, 2001, 1986). In particular, the differences are magniï¬ed when written language is compared to spoken face-to- face conversations, which are multi-modal and highly socially situated. As Biber and Finegan (1986) observed, pronouns, questions, and contradictions, as well as that-clauses and if-clauses, appear with a high frequency in face-to-face conversations. Forchini (2012) summarized these differences: â... studies show that face-to-face conversation is interpersonal, situation-dependent has no narrative concern or as Biber and Finegan (1986) put it, is a highly interactive, situated and immediate text type...â Due to these differences between spoken and written language, we will emphasize the distinction between dialogue corpora in written and spoken language in the following sections. | 1512.05742#28 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
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"id": "1606.03632"
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"id": "1611.09268"
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}
] |
1512.05742 | 29 | Similarly, dialogues involving visual and other modalities differ from dialogues without these modalities (Card et al., 1983; Goodwin, 1981). When a visual modality is available â for example, when two human interlucators converse face-to-face â body language and eye gaze has a signiï¬cant impact on what is said and how it is said (Gibson and Pick, 1963; Lord and Haith, 1974; Cooper, 1974; Chartrand and Bargh, 1999; de Kok et al., 2013). Aside from the visual modality, dialogue systems may also incorporate other situational modalities, including aspects of virtual environments (Rickel and Johnson, 1999; Traum and Rickel, 2002) and user proï¬les (Li et al., 2016).
# 3.2 Human-Human Vs. Human-Machine Corpora
Another important distinction between dialogue datasets resides in the types of interlocutors â notably, whether it involves interactions between two humans, or between a human and a computer2. The distinction is important because current artiï¬cial dialogue systems are signiï¬cantly constrained.
2. Machine-machine dialogue corpora are not of interest to us, because they typically differ signiï¬cantly from natural human language. Furthermore, user simulation models are outside the scope of this survey.
8 | 1512.05742#29 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
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"id": "1607.00070"
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"id": "1503.02364"
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"id": "1511.08099"
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"id": "1604.06045"
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"id": "1610.02891"
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"id": "1606.02689"
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"id": "1606.01269"
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{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 30 | 8
These systems do not produce nearly the same distribution of possible responses as humans do under equivalent circumstances. As stated by Williams and Young (2007):
(Human-human conversation) does not contain the same distribution of understanding errors, and humanâhuman turn-taking is much richer than human-machine dialog. As a result, human-machine dialogue exhibits very different traits than human-human dia- logue (Doran et al., 2001; Moore and Browning, 1992).
The expectation a human interlucator begins with, and the interface through which they interact, also affect the nature of the conversation (J. and D., 1988). | 1512.05742#30 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
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"id": "1503.02364"
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"id": "1604.06045"
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"id": "1610.02891"
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"id": "1606.02689"
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{
"id": "1606.01269"
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{
"id": "1605.03481"
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{
"id": "1506.05869"
}
] |
1512.05742 | 31 | The expectation a human interlucator begins with, and the interface through which they interact, also affect the nature of the conversation (J. and D., 1988).
For goal-driven settings, Williams and Young (2007) have previously argued against building data-driven dialogue systems using human-human dialogues: â... using human-human conversation data is not appropriate because it does not contain the same distribution of understanding errors, and because human-human turn-taking is much richer than human-machine dialog.â This line of reasoning seems particularly applicable to spoken dialogue systems, where speech recognition errors can have a critical impact on performance and therefore must be taken into account when learning the dialogue model. The argument is also relevant to goal-driven dialogue systems, where an effective dialogue model can often be learned using reinforcement learning techniques. Williams and Young (2007) also argue against learning from corpora generated between humans and existing dialogue systems: âWhile it would be possible to use a corpus collected from an existing spoken dialogue system, supervised learning would simply learn to approximate the policy used by that spoken dialogue system and an overall performance improvement would therefore be unlikely.â | 1512.05742#31 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
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{
"id": "1604.06045"
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"id": "1610.02891"
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"id": "1606.02689"
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{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 32 | Thus, it appears, for goal-driven spoken dialogue systems in particular, that the most effective strategy is learning online through interaction with real users. Nonetheless, there exists useful human-machine corpora where the interacting machine uses a stochastic policy that can generate sufï¬cient coverage of the task (e.g. enough good and enough bad dialogue examples) to allow an effective dialogue model to be learned. In this case, the goal is to learn a policy that is eventually better than the original stochastic policy used to generate the corpus through a process known as bootstrapping. | 1512.05742#32 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
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"id": "1510.03055"
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"id": "1607.00070"
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"id": "1503.02364"
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"id": "1511.08099"
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"id": "1604.06045"
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"id": "1610.02891"
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"id": "1606.02689"
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"id": "1606.01269"
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"id": "1605.03481"
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{
"id": "1506.05869"
}
] |
1512.05742 | 33 | In this survey we focus on data-driven learning from human-human and human-machine di- alogue corpora. Despite the advantages of learning online through interactions with real users, learning based on human-human dialogue corpora may be more suitable for open domain dialogue systems because they reï¬ect natural dialogue interactions. By natural dialogues, we mean conver- sations that are unconstrained and unscripted, e.g. between interlocutors who are not instructed to carry out a particular task, to follow a series of instructions, or to act out a scripted dialogue. In this setting, the dialogue process is relatively unaffected by researchers, e.g. the interlocutors are not in- terrupted by question prompts in the middle of a dialogue. As can be expected, such conversations include a signiï¬cant amount of turn-taking, pauses and common grounding phenomena (Clark and Brennan, 1991). Additionally, they are more diverse, and open up the possibility for the model to learn to understand natural language.
# 3.3 Natural Vs. Unnatural Corpora
The way in which a dialogue corpus is generated and collected can have a signiï¬cant inï¬uence on the trained data-driven dialogue system. In the case of human-human dialogues, an ideal corpus should closely resemble natural dialogues between humans. Arguably, this is the case when conversations
9 | 1512.05742#33 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
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"id": "1607.00070"
},
{
"id": "1503.02364"
},
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"id": "1511.08099"
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"id": "1604.06045"
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"id": "1610.02891"
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"id": "1606.01269"
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"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 34 | 9
between humans are recorded and transcribed, and when the humans in the dialogue represent the true population of users with whom the dialogue system is intended to interact. It is even better if they are unaware of the fact that they are being recorded, but this is not always possible due to ethical considerations and resource constraints.
Due to ethical considerations and resource constraints, researchers may be forced to inform the human interlocutors that they are being recorded or to setup artiï¬cial experiments in which they hire humans and instruct them to carry out a particular task by interacting with a dialogue system. In these cases, there is no guarantee that the interactions in the corpus will reï¬ect true interactions, since the hired humans may behave differently from the true user population. One factor that may cause behavioural differences is the fact that the hired humans may not share the same intentions and motivations as the true user population (Young et al., 2013). The unnaturalness may be further exacerbated by the hiring process, as well as the platform through which they interact. Such factors are becoming more prevalent as researchers increasingly rely on crowdsourcing platforms, such as Amazon Mechanical Turk, to collect and evaluate dialogue data (Jurcıcek et al., 2011). | 1512.05742#34 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
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{
"id": "1604.06045"
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"id": "1610.02891"
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"id": "1606.01269"
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"id": "1506.05869"
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] |
1512.05742 | 35 | In the case of Wizard-of-Oz experiments (Bohus and Rudnicky, 2008; Petrik, 2004), a human thinks (s)he is speaking to a machine, but a human operator is in fact controlling the dialogue system. This enables the generation of datasets that are closer in nature to the dialogues humans may wish to have with a good AI dialogue system. Unfortunately, such experiments are expensive and time- consuming to carry out. Ultimately the impact of any unnaturalness in the dialogues depends on the task and context in which the dialogue system is deployed.
# 3.4 Corpora from Fiction
It is also possible to use artiï¬cial dialogue corpora for data-driven learning. This includes cor- pora based on works of ï¬ction such as novels, movie manuscripts and audio subtitles. However, unlike transcribed human-human conversations, novels, movie manuscripts, and audio subtitles de- pend upon events outside the current conversation, which are not observed. This makes data-driven learning more difï¬cult because the dialogue system has to account for unknown factors. The same problem is also observed in certain other media, such as microblogging websites (e.g. Twitter and Weibo), where conversations also may depend on external unobserved events. | 1512.05742#35 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
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{
"id": "1610.02891"
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{
"id": "1606.02689"
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{
"id": "1606.01269"
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{
"id": "1605.03481"
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{
"id": "1506.05869"
}
] |
1512.05742 | 36 | Nevertheless, recent studies have found that spoken language in movies resembles spontaneous human spoken language (Forchini, 2009). Although movie dialogues are explicitly written to be spoken and contain certain artiï¬cial elements, many of the linguistic and paralinguistic features contained within the dialogues are similar to natural spoken language, including dialogue acts such as turn-taking and reciprocity (e.g. returning a greeting when greeted). The artiï¬cial differences that exist may even be helpful for data-driven dialogue learning since movie dialogues are more compact, follow a steady rhythm, and contain less garbling and repetition, all while still presenting a clear event or message to the viewer (Dose, 2013; Forchini, 2009, 2012). Unlike dialogues extracted from Wizard-of-Oz human experiments, movie dialogues span many different topics and occur in many different environments (Webb, 2010). They contain different actors with different intentions and relationships to one another, which could potentially allow a data-driven dialogue system to learn to personalize itself to different users by making use of different interaction patterns (Li et al., 2016).
10
# 3.5 Corpus Size | 1512.05742#36 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
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{
"id": "1610.02891"
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{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
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{
"id": "1506.05869"
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] |
1512.05742 | 38 | There are two primary perspectives on the importance of dataset size for building data-driven dialogue systems. The ï¬rst perspective comes from the machine learning literature: larger datasets place constraints on the dialogue model trained from that data. Datasets with few examples may require strong structural priors placed on the model, such as using a modular system, while large datasets can be used to train end-to-end dialogue systems with less a priori structure. The second comes from a statistical natural language processing perspective: since the statistical complexity of a corpus grows with the linguistic diversity and number of topics, the number of examples required by a machine learning algorithm to model the patterns in it will also grow with the linguistic diversity and number of topics. Consider two small datasets with the same number of dialogues in the domain of bus schedule information: in one dataset the conversations between the users and operator is natural, and the operator can improvise and chitchat; in the other dataset, the operator reads from a script to provide the bus information. Despite having the same size, the second dataset will have less linguistic diversity and not include chitchat topics. Therefore, it will be easier to | 1512.05742#38 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 39 | the bus information. Despite having the same size, the second dataset will have less linguistic diversity and not include chitchat topics. Therefore, it will be easier to train a data-driven dialogue system mimcking the behaviour of the operator in the second dataset, however it will also exhibit a highly pedantic style and not be able to chitchat. In addition to this, to have an effective discussion between any two agents, their common knowledge must be represented and understood by both parties. The process of establishing this common knowledge, also known as grounding, is especially critical to repair misunderstandings between humans and dialogue systems (Cahn and Brennan, 1999). Since the number of misunderstandings can grow with the lexical diversity and number of topics (e.g. misunderstanding the paraphrase of an existing word, or misunderstanding a rarely seen keyword), the number of examples required to repair these grow with linguistic diversity and topics. In particular, the effect of linguistic diversity has been observed in practice: Vinyals and Le (2015) train a simple encoder-decoder neural network on a proprietary dataset of technical support dialogues. Although it has a similar size and purpose as the Ubuntu Dialogue Corpus (Lowe et | 1512.05742#39 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1606.03632"
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1512.05742 | 40 | neural network on a proprietary dataset of technical support dialogues. Although it has a similar size and purpose as the Ubuntu Dialogue Corpus (Lowe et al., 2015a), the qualitative examples shown by Vinyals and Le (2015) are signiï¬cantly superior to those obtained by more complex models on the Ubuntu Corpus (Serban et al., 2017a). This result may likely be explained in part due to the fact that technical support operators often follow a comprehensive script for solving problems. As such, the script would reduce the linguistic diversity of their responses. | 1512.05742#40 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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"id": "1611.09268"
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1512.05742 | 41 | Furthermore, since the majority of human-human dialogues are multi-modal and highly am- biguous in nature (Chartrand and Bargh, 1999; de Kok et al., 2013), the size of the corpus may compensate for some of the ambiguity and missing modalities. If the corpus is sufï¬ciently large, then the resolved ambiguities and missing modalities may, for example, be approximated using latent stochastic variables (Serban et al., 2017b). Thus, we include corpus size as a dimension of analysis. We also discuss the beneï¬ts and drawbacks of several popular large-scale datasets in Section 5.1.
11
# 4. Available Dialogue Datasets | 1512.05742#41 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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"id": "1611.09268"
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"id": "1506.05869"
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] |
1512.05742 | 42 | 11
# 4. Available Dialogue Datasets
There is a vast amount of data available documenting human communication. Much of this data could be used â perhaps after some pre-processing â to train a dialogue system. However, cov- ering all such sources of data would be infeasible. Thus, we restrict the scope of this survey to datasets that have already been used to study dialogue or build dialogue systems, and to very large corpora of interactionsâthat may or may not be strictly considered dialogue datasetsâwhich could be leveraged in the near future to build more sophisticated data-driven dialogue models. We restrict the selection further to contain only corpora generated from spoken or written English, and to cor- pora which, to the best of our knowledge, either are publicly available or will be made available in the near future. We ï¬rst give a brief overview of each of the considered corpora, and later high- light some of the more promising examples, explaining how they could be used to further dialogue research.3
The dialogue datasets analyzed in this paper are listed in Tables 1-5. Column features indicate properties of the datasets, including the number of dialogues, average dialogue length, number of words, whether the interactions are between humans or with an automated system, and whether the dialogues are written or spoken. Below, we discuss qualitative features of the datasets, while statistics can be found in the aforementioned table.
# 4.1 Human-Machine Corpora | 1512.05742#42 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 43 | # 4.1 Human-Machine Corpora
As discussed in Subsection 3.2, an important distinction between dialogue datasets is whether they consist of dialogues between two humans or between a human and a machine. Thus, we begin by outlining some of the existing human-machine corpora in several categories based on the types of systems the humans interact with: Restaurant and Travel Information, Open-Domain Knowledge Retrieval, and Other Specialized systems. Note, we also include human-human corpora here where one human plays the role of the machine in a Wizard-of-Oz fashion.
# 4.1.1 RESTAURANT AND TRAVEL INFORMATION
One common theme in human-machine language datasets is interaction with systems which provide restaurant or travel information. Here weâll brieï¬y describe some human-machine dialogue datasets in this domain. | 1512.05742#43 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 44 | One of the most popular recent sources of such data has come from the datasets for structured dialogue prediction released in conjunction with the Dialog State Tracking Challenge (DSTC) (Williams et al., 2013). As the name implies, these datasets are used to learn a strategy for the Di- alogue State Tracker (sometimes called âbelief trackingâ), which involves estimating the intentions of a user throughout a dialog. State tracking is useful as it can increase the robustness of speech recognition systems, and can provide an implementable framework for real-world dialogue sys- tems. Particularly in the context of goal-oriented dialogue systems (such as those providing travel and restaurant information), state tracking is necessary for creating coherent conversational inter- faces. As such, the ï¬rst three datasets in the DSTCâreferred to as DSTC1, DSTC2, and DSTC3 respectivelyâare medium-sized spoken datasets obtained from human-machine interactions with
3. We form a live list of the corpora discussed in this work, along with links to downloads, at: http://breakend. github.io/DialogDatasets. Pull requests can be made to the Github repository (https://github. com/Breakend/DialogDatasets) hosting the website for continuing updates to the list of corpora.
12 | 1512.05742#44 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
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"id": "1607.00070"
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"id": "1503.02364"
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"id": "1506.05869"
}
] |
1512.05742 | 45 | 12
restaurant and travel information systems. All datasets provide labels specifying the current goal and desired action of the system.
DSTC1 (Williams et al., 2013) features conversations with an automated bus information in- terface, where users request bus routes from the system and the system responds with clarifying queries or the desired information. DSTC2 introduces changing user goals in a restaurant booking system, while trying to provide a desired reservation(Henderson et al., 2014b). DSTC3 introduces a small amount of labelled data in the domain of tourist information. It is intended to be used in conjunction with the DSTC2 dataset as a domain adaptation problem (Henderson et al., 2014a).
The Carnegie Mellon Communicator Corpus (Bennett and Rudnicky, 2002) also contains human-machine interactions with a travel booking system. It is a medium-sized dataset of interac- tions with a system providing up-to-the-minute ï¬ight information, hotel information, and car rentals. Conversations with the system were transcribed, along with the userâs comments at the end of the interaction. | 1512.05742#45 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
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{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 46 | The ATIS (Air Travel Information System) Pilot Corpus (Hemphill et al., 1990) is one of the ï¬rst human-machine corpora. It consists of interactions, lasting about 40 minutes each, between human participants and a travel-type booking system, secretly operated by humans. Unlike the Carnegie Mellon Communicator Corpus, it only contains 1041 utterances.
In the Maluuba Frames Corpus (El Asri et al., 2017), one user plays the role of a conversa- tional agent in a Wizard-of-Oz fashion, while the other user is tasked with ï¬nding available travel or vacation accommodations according to a pre-speciï¬ed task. The Wizard is provided with a knowl- edge database which recorded their actions. Semantic frames are annotated in addition to actions which the Wizard performed on the database to accompany a line of dialogue. In this way, the Frames corpus aims to track decision-making processes in travel- and hotel-booking through natu- ral dialog.
# 4.1.2 OPEN-DOMAIN KNOWLEDGE RETRIEVAL | 1512.05742#46 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 47 | # 4.1.2 OPEN-DOMAIN KNOWLEDGE RETRIEVAL
Knowledge retrieval and Question & Answer (QA) corpora are a broad distinction of corpora that we will not extensively review here. Instead, we include only those QA corpora which explicitly record interactions of humans with existing systems. The Ritel corpus (Rosset and Petel, 2006) is a small dataset of 528 dialogs with the Wizard-of-Oz Ritel platform. The projectâs purpose was to integrate spoken language dialogue systems with open-domain information retrieval systems, with the end goal of allowing humans to ask general questions and iteratively reï¬ne their search. The questions in the corpus mostly revolve around politics and the economy, such as âWho is currently presiding the Senate?â, along with some conversations about arts and science-related topics.
Other similar open-domain corpora in this area include WikiQA Yang et al. (2015) and MS MARCO Nguyen et al. (2016), which compile responses from automated Bing searches and hu- man annotators. However, these do not record dialogs, but rather simply gather possible responses to queries. As such, we wonât discuss these datasets further, but rather mention them brieï¬y as examples of other Open-Domain corpora in the ï¬eld.
13 | 1512.05742#47 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
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{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 48 | n o i t p i r c s e D # l a t o T # l a t o T # . g v A s c i p o T e p y T s d r o w f o s e u g o l a i d f o s n r u t f o m e t s y s n o i t a m r o f n i e d i r s u B M 7 3 . 0 0 0 , 5 1 6 5 . 3 1 s e l u d e h c s s u B n e k o p S ) 3 1 0 2 , . l a t e m e t s y s g n i k o o b t n a r u a t s e R K 2 3 4 0 0 0 , 3 8 8 . 7 s t n a r u a t s e R n e k o p S ) b 4 1 0 2 , . l a s t s i r u o t r o f n o i t a m r o f n I K 3 0 4 5 6 2 , 2 7 2 . 8 n o i t a m r o f n i t s i r u o T n e k o p S ) a 4 1 0 2 , . l a m e t s y s g n i k o o b d n a g n i n n a l p l e v a r T * M 2 1 8 4 | 1512.05742#48 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
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1512.05742 | 49 | , . l a m e t s y s g n i k o o b d n a g n i n n a l p l e v a r T * M 2 1 8 4 , 5 1 7 6 . 1 1 l e v a r T n e k o p S s u p r o C ) 2 0 0 2 m e t s y s g n i k o o b d n a g n i n n a l p l e v a r T * K 4 1 1 . 1 4 4 . 5 2 l e v a r T n e k o p S n o i t s e u q n i a m o d - n e p o d e t a t o n n a n A k 0 6 2 8 5 * 3 . 9 s c i p o T e s r e v i D / d e t c i r t s e r n U n e k o p S m e t s y s e u g o l a i d m e t s y s r e t u p m o c h t i n e k o p s g n i r e w s n a w t c a r e t n i s n a m u H * K 7 8 . 6 6 2 1 s c i t a m e h t a M n e k o p S ) 6 0 0 2 g n i v o r p m e r | 1512.05742#49 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 50 | K 7 8 . 6 6 2 1 s c i t a m e h t a M n e k o p S ) 6 0 0 2 g n i v o r p m e r o e h t l a c i t a m e h t a m o d o t ) 4 0 0 2 , . l a t e . s t n e m t n i o p p a g n i l u d e h c s r o f m e t s y s A * K 9 6 7 4 4 0 . 4 1 g n i l u d e h c S t n e m t n i o p p A n e k o p S s n o i t a t o n n a t c a e u g o l a i d s e d u l c n I ) 0 1 0 2 . s m e t s y s e u g o l a i d n e v i r d - l a o g r o F â 9 6 3 1 5 1 n o i t a c a V & l e v a r T & A Q , t a h C s n o i t c a d n a d e l e b a l s e m a r f c i t n a m e S g n i k o o B n o i t a d n e m m o c e R . d e t a t o n n a | 1512.05742#50 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 51 | f c i t n a m e S g n i k o o B n o i t a d n e m m o c e R . d e t a t o n n a e s a b - e g d e l w o n k a n o n e k a t . e c n a r e t t u r e p s d r o w f o r e b m u n e g a r e v a e h t n o d e s a b d e t a m i x o r p p a e r a s r e b m u n ) * ( d e r r a t S . s t e s a t a d e u g o l a i d . n a m u h a y b d e t a r e p o y l t e r c e s s i e n i h c a m e h t e r e h w , s e u g o l a i d z O - f o - d r a z i W e t a c i d n i ) â ( h t i | 1512.05742#51 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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w d e k r a m s t e s a t a D | 1512.05742#52 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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"id": "1606.03632"
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"id": "1506.05869"
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] |
1512.05742 | 53 | n o i t p i r c s e D l a t o T # l a t o T # l a t o T s c i p o T h t g n e l s d r o w f o s e u g o l a i d f o y l l a b r e v e t a r o b a l l o c t s u m s r e k a e p s h c i h w n i k s a T P A L H m o r f s e u g o l a i D s r h 8 1 k 7 4 1 8 2 1 g n i c u d o r p e R - p a M . s r e h t o e h t n o d e t n i r p e t u o r a p a m s t n a p i c i t r a p e n o n o e c u d o r p e r o t k s a T . s n o i t a c o l n i a t r e c d n ï¬ o t e n o h p e l e t r e v o g n i t a r o b a l l o c e l p o e P s r h 3 3 * k 0 0 3 6 3 n o i t a c o L k s a T g n i d n i F e c n i v n o c | 1512.05742#53 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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1512.05742 | 54 | P s r h 3 3 * k 0 0 3 6 3 n o i t a c o L k s a T g n i d n i F e c n i v n o c o t s e i r t s g n i l e e f n e e r g - o r p g n o r t s ) y l e n i u n e g ( h t i w r e d a u s r e p A s r h 4 * k 5 3 8 e l y t s e f i L . s e l y t s e f i l n e e r g e r o m g n i t p o d a r e d i s n o c o t s e e d a u s r e p ) 7 0 0 2 , . l a d e n i a r t s n o c h c a e , s e t a b e d e l y t s - d r o f x O n i s c i p o t s u o i r a V * s r h 0 0 2 M 8 . 1 8 0 1 s e t a b e D . s e t a b e d - t s o p d n a - e r p d e d i v o r p s n o i n i p o e c n e i d u A . t c e j b u s e n o o t e | 1512.05742#54 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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1512.05742 | 55 | r p d e d i v o r p s n o i n i p o e c n e i d u A . t c e j b u s e n o o t e s u o H e t i h W d n a s g n i t e e m y t l u c a f m o r f s n o i t c a r e t n I * s r h 0 2 2 M 2 0 0 2 n o i t a c u d E , s c i t i l o P n e k o p S . s e c n e r e f n o c s s e r p ) 0 0 0 2 , w o l r a B a d n a , c i p o t l a c i t i l o p a n o n o i s s u c s i d a : s t n e m i r e p x e o w T s r h 1 1 * k 0 0 1 4 5 s e m a G , s c i t i l o P . e m a g g n i y a l p - e l o r s n o i t a t o n n a h t i w e m a g g n i y a l p - e l o r f l o w e r e W f o g n i d r o c e r A s r h 7 * k 0 6 5 | 1512.05742#55 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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1512.05742 | 56 | n i y a l p - e l o r f l o w e r e W f o g n i d r o c e r A s r h 7 * k 0 6 5 1 e m a G g n i y a l P - e l o R s u p r o C . s s e r g o r p e m a g o t d e t a l e r ) 0 1 0 2 r o t a r e p o n a h t i w s n o i t a s r e v n o c g n i d l o h e l i h w d e d r o c e r e r e w s r e s U s r h 0 5 * k 0 5 4 0 0 1 l a n o i t o m E . s n o i t c a e r l a n o i t o m e e k o v e o t d e n g i s e d s e l o r s t p o d a o h w s n o i t a s r e v n o C . e p y k S r e v o e g n a h c x e n o i t a m r o f n i t s i r u o T s r h 1 2 k 3 7 2 5 3 t s i r u o T ) 6 1 0 2 . s n o r t a p d n a | 1512.05742#56 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 57 | s i r u o T s r h 1 2 k 3 7 2 5 3 t s i r u o T ) 6 1 0 2 . s n o r t a p d n a s n a i r a r b i l n e e w t e b s n o i t c a r e t n i e n o h p e l e T * 0 4 1 K 1 2 2 8 s e i r i u q n I y r a r b i L , ) s t i n u e s r u o c s i d ( s e m a r f , s c i p o t n o i s s u c s i d , s t c a e u g o l a i d d e t a t o n n A ) 4 1 0 2 . s r i a p r e w s n a - n o i t s e u q , f l e s t i t c e j o r p s u p r o c e h t : e d u l c n i s c i p o T . s g n i t e e m I S C I f o s g n i d r o c e R s r h 2 7 * K 1 1 5 7 s g n i t e e M I S C I f o s e i r o e h t d n a g n i s s e c o r p | 1512.05742#57 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 58 | K 1 1 5 7 s g n i t e e M I S C I f o s e i r o e h t d n a g n i s s e c o r p e g a u g n a l l a r u t a n , n o i t i n g o c e r h c e e p s c i t a m o t u a . s t o p s t o h d n a , s r i a p r e w s n a - n o i t s e u q , s t c a e u g o l a i D . e g a u g n a l . s e t u o r t h g i e r f d a o r l i a r f o g n i n n a l p e v i t a r o b a l l o C s r h 5 . 6 K 5 5 8 9 t h g i e r F d a o r l i a R g n i n n a l P e t u o R ) 5 9 9 1 . t c e j o r p l i b o m b r e V e h t r o f d e t c e l l o c a t a d h c e e p s s u o e n a t n o p S s r H 8 3 K 0 7 2 6 2 7 t n e m t n i o | 1512.05742#58 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 59 | t a d h c e e p s s u o e n a t n o p S s r H 8 3 K 0 7 2 6 2 7 t n e m t n i o p p A . e s e n a p a J d n a , n a m r e G , h s i l g n E n i s i s u p r o c l l u F g n i l u d e h c S . s c i t s i t a t s h s i l g n E w o h s y l n o e W h c e e p s h s i l g n E f o e t a r e g a r e v a e h t n o d e s a b s e t a m i t s e e r a s r e b m u n ) * ( d e r r a t S . ) l m t h . y t i l a u q / l a i r o t u t / d o r p e c i o v / s l a i r o t u t / s v c n / g r o . s v c n . w w w ( h c e e p S d n a e c i o V r o f r e t n e C | 1512.05742#59 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 60 | e m a N
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have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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1512.05742 | 61 | , n a j n a r a t t i h C d n a
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: 2 | 1512.05742#61 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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1512.05742 | 63 | n o i t p i r c s e D l a t o T # l a t o T # l a t o T s c i p o T h t g n e l s d r o w f o s e u g o l a i d f o s c i p o t d e ï¬ i c e p s - e r p n o s n o i t a s r e v n o c e n o h p e l e T * s r h 0 0 3 M 3 0 0 4 , 2 s c i p o T l a u s a C ) 2 9 9 1 , . l a t e y e r f d o G ( r o s s e n i s u b l a m r o f m o r f , s t x e t n o c y n a m s e u g o l a i d h s i t i r B * s r h 0 0 0 , 1 M 0 1 4 5 8 s c i p o T l a u s a C ) C N B ( s u p r o C . s n i - e n o h p d n a s w o h s o i d a r o t s g n i t e e m . s d n e i r f e s o l c r o s r e b m e m y l i m a f n e e w | 1512.05742#63 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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1512.05742 | 64 | s g n i t e e m . s d n e i r f e s o l c r o s r e b m e m y l i m a f n e e w t e b s n o i t a s r e v n o c e n o h p e l e T s r h 0 6 * k 0 4 5 0 2 1 s c i p o T l a u s a C ) 7 9 9 1 , . l a t e n r e h t u o S a h t i w s n a c i r e m A n e e w t e b s n o i t a s r e v n o c e n o h p e l e T s r h 0 2 * k 0 8 1 0 6 s c i p o T l a u s a C ) 6 9 9 1 . 3 9 9 1 n i d e d r o c e r k l a t e g a n e e t s u o e n a t n o p S s r h 5 5 k 0 0 5 0 0 1 d e t c i r t s e r n U n o d n o L f o . y l t e r c e s d e d r o c e r e r e w s n o i t a s r e v n o C ) 5 9 9 1 , s a h c u s , s t | 1512.05742#64 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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1512.05742 | 65 | d e d r o c e r e r e w s n o i t a s r e v n o C ) 5 9 9 1 , s a h c u s , s t x e t n o c l a m r o f n i f o y t e i r a v e d i w m o r f s e u g o l a i d h s i t i r B * s r h 0 5 5 M 5 â s c i p o T l a u s a C m a h g n i t t o N d n a . c t e , s t n a r u a t s e r , s n o l a s r i a h h s i l g n E n i ) 8 9 9 1 e l p o e p f o p u o r g a n e e w t e b n o i t c a r e t n i l a r u t a n f o s r u o h l a r e v e S s r h 8 * k 0 7 2 d e t c i r t s e r n U s u p r o C n o i t a s r e v n o C ) 3 1 0 2 . s g n i d r o c e r g n i t e e m e c a f - o t - e c a F s r h 0 0 1 * k 0 | 1512.05742#65 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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"id": "1606.03632"
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1512.05742 | 66 | 2 . s g n i d r o c e r g n i t e e m e c a f - o t - e c a F s r h 0 0 1 * k 0 0 9 5 7 1 s g n i t e e M , s n o i t a s r e v n o c l a r u t a n d e t p i r c s n u h t i w e s a b a t a d l a u s i v - o i d u A n i m 0 5 1 * k 0 2 0 3 d e t c i r t s e r n U . s n o i t a t o n n a l a u s i v g n i d u l c n i ) 3 1 0 2 , . l a t e o e d i v D 3 h t i w b D C C e h t f o n o i s r e v A n i m 7 1 * k 5 . 2 7 1 d e t c i r t s e r n U e s a b a t a D n o i t a s r e v n o C ) 5 1 0 2 , . l a t e r e t n e v e d n a V ( c i l b u p d n a , e n o h p e l e t , e c a f - o t - e c a f f o | 1512.05742#66 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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1512.05742 | 67 | e d n a V ( c i l b u p d n a , e n o h p e l e t , e c a f - o t - e c a f f o n o i t c e l e S * s r h 0 8 k 0 0 8 0 8 2 s c i p o T l a u s a C f o . n i a t i r B m o r f e u g o l a i d n o i s s u c s i d ) 6 0 0 2 , s i l l a , s e i r o m e m r i e h t t u o b a g n i k l a t e v o b a r o 0 6 d e g a e l p o e p f o e u g o l a i D s r h 0 6 k 0 0 8 4 1 3 s c i p o T l a u s a C e h t f o y r u t n e c a m o r f e d i s y r t n u o c e h t f o e r o l k l o f e h t d n a k r o w , s e i l i m a f ) 9 9 9 1 e h t r o f d e z i n a g r o e s a b a t a d l a n o i t a n r e t n I * s | 1512.05742#67 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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1512.05742 | 68 | 9 1 e h t r o f d e z i n a g r o e s a b a t a d l a n o i t a n r e t n I * s r h 0 0 0 , 1 M 0 1 K 1 1 d e t c i r t s e r n U . n o i t i s i u q c a e g a u g n a l d n o c e s d n a t s r ï¬ f o y d u t s ) 5 8 9 1 , e v i t a t n e s e r p e r s w e i v r e t n i d n a s n o i t a s r e v n o c , s e v i t a r r a N * s r h 2 K 0 2 5 9 s c i p o T l a u s a C d n a . a n i l o r a C h t r o N , y t n u o C g r u b n e l k c e M f o s t n e d i s e r e h t f o ) C C N C ) 4 0 0 2 i t s e e r a s r e b m u n ) * ( d e r r a t S . s t e s a t a d e u g o l a i d n e k o p s | 1512.05742#68 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 69 | h s i l g n E n a c i r e m A D N E I R F L L A C
h s i l g n E n a c i r e m A E M O H L L A C
e s a b a t a D n o i t a s r e v n o C
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e g a u g n a L e g a n e e T | 1512.05742#69 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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, e d I d n a | 1512.05742#70 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 72 | n o i t p i r c s e D # l a t o T # l a t o T # l a t o T # l a t o T s c i p o T s d r o w f o s k r o w f o s e u g o l a i d f o s e c n a r e t t u f o . s m l ï¬ n a c i r e m A f o s t p i r c s e i v o M M 6 3 5 7 K 2 3 1 k 4 6 7 e i v o M s e u g o l a i d e m o c o t d e r e t l ï¬ e r a h c i h w s e c n a r e t t u f o s e l p i r T M 3 1 4 1 6 K 5 4 2 k 6 3 7 e i v o M . s e l p i r t X Y X m o r f - - s e u g o l a i d s t p i r c s f o s t e s b u s o w T * M 6 1 0 0 5 , 1 â K 3 6 2 * M 1 e i v o M . ) s m l ï¬ n a c i r e m A / h s i t i r B d e x i m 0 0 5 d n a s m | 1512.05742#72 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 73 | v o M . ) s m l ï¬ n a c i r e m A / h s i t i r B d e x i m 0 0 5 d n a s m l ï¬ n a c i r e m A 0 0 0 1 ( s t p i r c s d e t a t o n n a , s t p i r c s m l ï¬ m o r f s n o i t a s r e v n o c t r o h S * M 9 7 1 6 K 0 2 2 K 5 0 3 e i v o M . a t a d a t e m r e t c a r a h c h t i w s e u g o l a i d ) 1 1 0 2 , e e L d n a e m o c o t d e r e t l ï¬ e r a h c i h w s e c n a r e t t u f o s e l p i r T * M 2 6 8 7 , 1 K 7 8 k 3 7 1 e i v o M . s e l p i r t X Y X m o r f - - s e u g o l a i d . s a r e p o p a o s n a c i r e m A f o s t p i r c s n a r T M 0 0 1 0 0 0 , 2 | 1512.05742#73 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 74 | s a r e p o p a o s n a c i r e m A f o s t p i r c s n a r T M 0 0 1 0 0 0 , 2 2 â M 2 . 1 * M 0 1 w o h s V T s t p i r c s d n a ) y r o e h T g n a B g i B ( y d e m o c a m o r f s t p i r c s V T * k 0 0 6 1 9 1 â K 0 1 * k 0 6 w o h s V T c i t s i u g n i l r o f . w o h s ) s e n o r h T f o e m a G d e t a t o n n a , b D S M I m o r f ( a m a r d s t p i r c S M 6 9 . 2 6 8 K 1 5 1 k 4 6 6 s t p i r c s e i v o M . s e p y t e h c r a r e t c a r a h c d n a s e r u t c u r t s s t p i r c s m o r f s r i a p e s n o p s e r - n o i t c a r e t n i d e n g i l A M 0 2 4 8 1 , 6 M | 1512.05742#74 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 75 | r i a p e s n o p s e r - n o i t c a r e t n i d e n g i l A M 0 2 4 8 1 , 6 M 5 3 . 3 M 7 . 6 e i v o M . s e l t i t b u s e i v o m s e l t i t b u s . d e n g i l a - r e k a e p s t o n e r a h c i h w s e l t i t b u s e i v o M B 1 7 0 9 , 7 0 2 â M 6 3 * M 0 4 1 e i v o M s e l t i t b u s ) 0 6 7 1 â 0 6 5 1 ( m o r f s k r o w l a n o i t c ï¬ d e t p i r c s s u o i r a V M 2 1 . 7 7 1 â â s k r o W n e t t i r W . s g n i d e e c o r p l a i r t t r u o c s a l l e w s a s g n i d e e c o r P l a i r T & r e p s e u g o l a i d f o r e b m u n e g a r e v a n o d e s a b s e | 1512.05742#75 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 76 | i r T & r e p s e u g o l a i d f o r e b m u n e g a r e v a n o d e s a b s e t a m i t s e e t a c i d n i ) â ( h t i w d e t o n e d s e i t i t n a u Q . s t e s a t a d e s e h t n i d e t a r a p e s y l t i c i l p x e e b t o n y a m s e u g o l a i D . s u p r o c e h t n i s k r o w r o s t p i r c s f o r e b m u n e h t d n a ) 2 1 0 2 . ) s e t u n i m 6 3 ( e m i t n u r w o h s V T e g a r e v a o t ) s e t u n i m 2 1 1 ( e m i t n u r m l ï¬ e g a r e v a f o o i t a r e h t n o d e s a b d e t s u j d a d e s a b d e t a m i t s e e r a s e i t i t n a u q ) * ( d e r | 1512.05742#76 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 78 | . s t e s a t a d e u g o l a i d d e t p i r c s n a m u h - n a m u H
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r e k l a | 1512.05742#78 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 81 | SOLUNOWOS ae splOM YsI[suq Jodoid âe1od109 SWS pue Dy] Sv Yyons âeJodso9 urey99 JO nq â(aoeds ev Aq pomo][o} pue papacaid gouonbes v aq JSNUI pJOM B â9'1) Sadeds UO paseq S]UNOD pJOM SuIsn paynduios ore saturn (,.) paueyg âsjaseyep onsoyeip usyM âso[din o8pa[Mouy sv vyepry}oU! SIAOUT UONepUdUTWIODOY Sapnjouy âsuIa}SAs ONSoyeIp USALIP-[BOS 10] WSS8I ANTE ee SOTAOJ RB WO âTeyD *(pajoe.yxe sanSoyerp ou) sso] DUI wo}skg wos podeios weans yD gx 49901 QI8EE | Suneiodg mungy yy âOUI Uo weeqs eyo nyuNqA wayskg Wo} pajoe.nxe sansoyeiq WOOL MOE ILL Sunesodg munqn, | 1512.05742#81 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 82 | Uo weeqs eyo nyuNqA wayskg Wo} pajoe.nxe sansoyeiq WOOL MOE ILL Sunesodg munqn, yey âsordoy MATAIOVUT pure âTeonttod â[e1ouad noge suOTIES.AUOD ws al OzS syse} [R1I00g yeyo âsuomtsod Jesoul 10 yeontyod aytoads jnoge sayeqoq WEL MII SPS sontog wni0, â\uaWIdeIesIp Jo (aserydesed *3'9) yuaUIIaIse JO (SIOZ ad4q Jey payejouuy âsUOTRSIOAUOD WINIO} aJeqQaq AkIID Wr'l MOI TZ poloLnsoiuy wmni0j srayeqag âparejouur [ag] pue od) juoweasy âspkary} WINJOJ SuOISsnosIq IpedryIAA pur [eUINOsOATT SOIT TR TZ poloLnsoiuy wniog | saseg yey PLIOM spre, Surdeyd siakvjd | 1512.05742#82 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 83 | SOIT TR TZ poloLnsoiuy wniog | saseg yey PLIOM spre, Surdeyd siakvjd usaayjoqg suoles1aAuoZ MTZ 9971 Ise SULIO) OUD yey âuRIe_ JO SIOTMES, OWS oY) UT sioke|d usamjaq suoTRsIoAUOZD - IZ S6 SULIO} OWED yeyo âyey [eJoUNs Jo âsppoiqns asnge oNsaulop JoyjIe WoL s}sod Jppoy WEOI-W6I â¬ELIZ â¬S'LT djoy asnqy wni0, sndiog âJIPP2Y SSO1OV SJUOUTUIOD G/T] - - - poornsomuy) wmni0, âsiskjeue Sumy UIA âsiasn OM] U99MI9qQ PI}d9]]OI SaSeSSoUl SIS #899â08S ME SI poornsomuy) SaSeSSoU SINS (6002 ssunsod winioj JONAS) aL 4098LP L89 poornsomuy) SO[QOINI JONIML | 1512.05742#83 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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( | 1512.05742#85 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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# s u p r o C | 1512.05742#86 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 87 | ) a 5 1 0 2
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# f o | 1512.05742#87 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
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1512.05742 | 88 | M 1 2
.
t a h t
# e t o N
# . s u p r o c
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d n a | 1512.05742#88 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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"id": "1511.06931"
},
{
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1512.05742 | 89 | # e h t
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. p u o r g s w e n r e p d e t c e l l o c
s t s o p f o r e b m u n e g a r e v a | 1512.05742#89 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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1512.05742 | 90 | s t s o p f o r e b m u n e g a r e v a
_ t i d d e r _ e l b a l i a v a _ y l c i l b u p _ y r e v e _ e v a h _ i / 7 g l x b 3 / s t n e m m o c / s t e s a t a d / r / m o c . t i d d e r . w w w / / : s p t t h
# : o t
# s r e f e r
) ⦠(
. s d r o w s a
/ t n e m m o c
# 4.1.3 OTHER
The DIALOG mathematical proof dataset (Wolska et al., 2004) is a Wizard-of-Oz dataset in- volving an automated tutoring system that attempts to advise students on proving mathematical theorems. This is done using a hinting algorithm that provides clues when students come up with an incorrect answer. At only 66 dialogues, the dataset is very small, and consists of a conglomeration of text-based interactions with the system, as well as think-aloud audio and video footage recorded by the users as they interacted with the system. The latter was transcribed and annotated with simple speech acts such as âsignaling emotionsâ or âself-addressingâ. | 1512.05742#90 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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"id": "1604.06045"
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"id": "1606.02689"
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"id": "1606.01269"
},
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"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 91 | The MATCH corpus (Georgila et al., 2010) is a small corpus of 447 dialogues based on a Wizard-of-Oz experiment, which collected 50 young and old adults interacting with spoken dialogue systems. These conversations were annotated semi-automatically with dialogue acts and âInforma- tion State Updateâ (ISU) representations of dialogue context. The corpus also contains information about the usersâ cognitive abilities, with the motivation of modeling how the elderly interact with dialogue systems.
# 4.2 Human-Human Spoken Corpora
Naturally, there is much more data available for conversations between humans than conversations between humans and machines. Thus, we break down this category further, into spoken dialogues (this section) and written dialogues (Section 4.3). The distinction between spoken and written dia- logues is important, since the distribution of utterances changes dramatically according to the nature of the interaction. As discussed in Subsection 3.1, spoken dialogues tend to be more colloquial and generally well-formed as the user speaks in train-of-thought manner; they also tend to use shorter words and phrases. Conversely, in written communication, users have the ability to reï¬ect on what they are writing before they send a message. Written dialogues can also contain spelling errors or abbreviations, though, which are generally not transcribed in spoken dialogues. | 1512.05742#91 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
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"id": "1510.03055"
},
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"id": "1607.00070"
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"id": "1503.02364"
},
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"id": "1511.08099"
},
{
"id": "1604.06045"
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"id": "1610.02891"
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"id": "1606.02689"
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"id": "1606.01269"
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{
"id": "1605.03481"
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{
"id": "1506.05869"
}
] |
1512.05742 | 92 | # 4.2.1 SPONTANEOUS SPOKEN CORPORA
We ï¬rst introduce datasets in which the topics of conversation are either casual, or not pre-speciï¬ed in any way. We refer to these corpora as spontaneous, as we believe they most closely mimic spontaneous and unplanned spoken interactions between humans.
Perhaps one of the most inï¬uential spoken corpora is the Switchboard dataset (Godfrey et al., 1992). This dataset consists of approximately 2,500 dialogues from phone calls, along with word- by-word transcriptions with about 500 total speakers. A computer-driven robot operator system introduced a topic for discussion between two participants, and recorded the resulting conversation. About 70 casual topics were provided, of which about 50 were frequently used. The corpus was originally designed for training and testing various speech processing algorithms; however, it has since been used for a wide variety of other tasks, including the modeling of dialogue acts such as âstatementâ, âquestionâ, and âagreementâ (Stolcke et al., 2000). | 1512.05742#92 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
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"id": "1604.06045"
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"id": "1610.02891"
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"id": "1606.02689"
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"id": "1606.01269"
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"id": "1605.03481"
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{
"id": "1506.05869"
}
] |
1512.05742 | 93 | Another important dataset is the British National Corpus (BNC) (Leech, 1992), which contains approximately 10 million words of dialogue. These were collected in a variety of contexts ranging from formal business or government meetings, to radio shows and phone-ins. Although most of the conversations are spoken in nature, some of them are also written. BNC covers a large number of sources, and was designed to represent a wide cross-section of British English from the late twentieth century. The corpus also includes part-of-speech (POS) tagging for every word. The vast
19
array of settings and topics covered by this corpus renders it very useful as a general-purpose spoken dialogue dataset.
Other datasets have been collected for the analysis of spoken English over the telephone. The CALLHOME American English Speech Corpus (Canavan et al., 1997) consists of 120 such conversations totalling about 60 hours, mostly between family members or close friends. Similarly, the CALLFRIEND American English-Non-Southern Dialect Corpus (Canavan and Zipperlen, 1996) consists of 60 telephone conversations lasting 5-30 minutes each between English speakers in North America without a Southern accent. It is annotated with speaker information such as sex, age, and education. The goal of the project was to support the development of language identiï¬cation technologies, yet, there are no distinguishing features in either of these corpora in terms of the topics of conversation. | 1512.05742#93 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
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{
"id": "1510.03055"
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] |
1512.05742 | 94 | An attempt to capture exclusively teenage spoken language was made in the Bergen Corpus of London Teenager Language (COLT) (Haslerud and Stenstr¨om, 1995). Conversations were recorded surreptitiously by student ârecruitsâ, with a Sony Walkman and a lapel microphone, in order to obtain a better representation of teenager interactions âin-the-wildâ. This dataset has been used to identify trends in language evolution in teenagers (Stenstr¨om et al., 2002).
The Cambridge and Nottingham Corpus of Discourse in English (CANCODE) (McCarthy, 1998) is a subset of the Cambridge International Corpus, containing about 5 million words collected from recordings made throughout the islands of Britain and Ireland. It was constructed by Cam- bridge University Press and the University of Nottingham using dialogue data on general topics between 1995 and 2000. It focuses on interpersonal communication in a range of social contexts, varying from hair salons, to post ofï¬ces, to restaurants. This has been used, for example, to study language awareness in relation to spoken texts and their cultural contexts (Carter, 1998). In the dataset, the relationships between speakers (e.g. roommates, strangers) is labeled and the interac- tion type is provided (e.g. professional, intimate). | 1512.05742#94 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
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1512.05742 | 95 | Other works have attempted to record the physical elements of conversations between humans. To this end, a small corpus entitled d64 Multimodal Conversational Corpus (Oertel et al., 2013) was collected, incorporating data from 7 video cameras, and the registration of 3-D head, torso, and arm motion using an Optitrack system. Signiï¬cant effort was made to make the data collection process as non-intrusiveâand thus, naturalisticâas possible. Annotations were made to attempt to quantify overall group excitement and pairwise social distance between participants.
A similar attempt to incorporate computer vision features was made in the AMI Meeting Cor- pus (Renals et al., 2007), where cameras, a VGA data projector capture, whiteboard capture, and digital pen capture, were all used in addition to speech recordings for various meeting scenarios. As with the d64 corpus, the AMI Meeting Corpus is a small dataset of multi-participant chats, that has not been disentangled into strict dialogue. The dataset has often been used for analysis of the dynamics of various corporate and academic meeting scenarios. | 1512.05742#95 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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{
"id": "1611.09268"
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] |
1512.05742 | 96 | In a similar vein, the Cardiff Conversation Database (CCDb) (Aubrey et al., 2013) is an audio- visual database containing unscripted natural conversations between pairs of people. The original dataset consisted of 30 ï¬ve minute conversations, 7 of which were fully annotated with transcrip- tions and behavioural annotations such as speaker activity, facial expressions, head motions, and smiles. The content of the conversation is an unconstrained discussion on topics such as movies. While the original dataset featured 2D visual feeds, an updated version with 3D video has also been derived, called the 4D Cardiff Conversation Database (4D CCDb) (Vandeventer et al., 2015).
20
This version contains 17 one-minute conversations from 4 participants on similarly un-constrained topics. | 1512.05742#96 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
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"id": "1607.00070"
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"id": "1606.01269"
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1512.05742 | 97 | 20
This version contains 17 one-minute conversations from 4 participants on similarly un-constrained topics.
The Diachronic Corpus of Present-Day Spoken English (DCPSE) (Aarts and Wallis, 2006) is a parsed corpus of spoken English made up of two separate datasets. It contains more than 400,000 words from the ICE-GB corpus (collected in the early 1990s) and 400,000 words from the London- Lund Corpus (collected in the late 1960s-early 1980s). ICE-GB refers to the British component of the International Corpus of English (Greenbaum and Nelson, 1996; Greenbaum, 1996) and contains both spoken and written dialogues from English adults who have completed secondary education. The dataset was selected to provide a representative sample of British English. The London-Lund Corpus (Svartvik, 1990) consists exclusively of spoken British conversations, both dialogues and monologues. It contains a selection of face-to-face, telephone, and public discussion dialogues; the latter refers to dialogues that are heard by an audience that does not participate in the dialogue, in- cluding interviews and panel discussions that have been broadcast. The orthographic transcriptions of the datasets are normalised and annotated according to the same criteria; ICE-GB was used as a gold standard for the parsing of DCPSE. | 1512.05742#97 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
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"id": "1607.00070"
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"id": "1606.01269"
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] |
1512.05742 | 98 | The Spoken Corpus of the Survey of English Dialects (Beare and Scott, 1999) consists of 1000 recordings, with about 0.8 million total words, collected from 1948-1961 in order to document various existing English dialects. People aged 60 and over were recruited, being most likely to speak the traditional âuncontaminatedâ dialects of their area and encouraged to talk about their memories, families, work, and their countryside folklore.
The Child Language Data Exchange System (CHILDES) (MacWhinney and Snow, 1985) is a database organized for the study of ï¬rst and second language acquisition. The database contains 10 million English words and approximately the same number of non-English words. It also contains transcripts, with occasional audio and video recordings of data collected from children and adults learning both ï¬rst and second languages, although the English transcripts are mostly from children. This corpus could be leveraged in order to build automated teaching assistants. | 1512.05742#98 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
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"id": "1510.03055"
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{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 99 | The expanded Charlotte Narrative and Conversation Collection (CNCC), a subset of the ï¬rst release of the American National Corpus (Reppen and Ide, 2004), contains 95 narratives, conver- sations and interviews representative of the residents of Mecklenburg County, North Carolina and its surrounding communities. The purpose of the CNCC was to create a corpus of conversation and conversational narration in a âNew Southâ city at the beginning of the 21st century, that could be used as a resource for linguistic analysis. It was originally released as one of several collections in the New South Voices corpus, which otherwise contained mostly oral histories. Information on speaker age and gender in the CNCC is included in the header for each transcript.
# 4.2.2 CONSTRAINED SPOKEN CORPORA | 1512.05742#99 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 100 | # 4.2.2 CONSTRAINED SPOKEN CORPORA
Next, we discuss domains in which conversations only occur about a particular topic, or intend to solve a speciï¬c task. Not only is the topic of the conversation speciï¬ed beforehand, but participants are discouraged from deviating off-topic. As a result, these corpora are slightly less general than their spontaneous counterparts; however, they may be useful for building goal-oriented dialogue systems. As discussed in Subsection 3.3, this may also make the conversations less natural. We can further subdivide this category into the types of topics they cover: path-ï¬nding or planning tasks, persuasion tasks or debates, Q&A or information retrieval tasks, and miscellaneous topics.
21
Collaborative Path-Finding or Planning Tasks Several corpora focus on task planning or path- ï¬nding through the collaboration of two interlocutors. In these corpora typically one person acts as the decision maker and the other acts as the observer. | 1512.05742#100 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 101 | A well-known example of such a dataset is the HCRC Map Task Corpus (Anderson et al., 1991), that consists of unscripted, task-oriented dialogues that have been digitally recorded and transcribed. The corpus uses the Map Task (Brown et al., 1984), where participants must collab- orate verbally to reproduce a route on one of the participantâs map on the map of another partic- ipant. The corpus is fairly small, but it controls for the familiarity between speakers, eye contact between speakers, matching between landmarks on the participantsâ maps, opportunities for con- trastive stress, and phonological characteristics of landmark names. By adding these controls, the dataset attempts to focus on solely the dialogue and human speech involved in the planning process. The Walking Around Corpus (Brennan et al., 2013) consists of 36 dialogues between people communicating over mobile telephone. The dialogues have two parts: ï¬rst, a âstationary partnerâ is asked to direct a âmobile partnerâ to ï¬nd 18 destinations on a medium-sized university campus. The stationary partner is equipped with a map marked with the target destinations accompanied by photos | 1512.05742#101 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 102 | partnerâ to ï¬nd 18 destinations on a medium-sized university campus. The stationary partner is equipped with a map marked with the target destinations accompanied by photos of the locations, while the mobile partner is given a GPS navigation system and a camera to take photos. In the second part, the participants are asked to interact in-person in order to duplicate the photos taken by the mobile partner. The goal of the dataset is to provide a testbed for natural lexical entrainment, and to be used as a resource for pedestrian navigation applications. | 1512.05742#102 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 103 | The TRAINS 93 Dialogues Corpus (Heeman and Allen, 1995) consists of recordings of two interlocutors interacting to solve various planning tasks for scheduling train routes and arranging railroad freight. One user acts the role of a planning assistant system and the other user acts as the coordinator. This was not done in a Wizard-of-Oz fashion, and as such is not considered a Human-Machine corpus. 34 different interlocutors were asked to complete 20 different tasks such as: âDetermine the maximum number of boxcars of oranges that you could get to Bath by 7 AM tomorrow morning. It is now 12 midnight.â The person playing the role of the planning assistant was provided with access to information that is needed to solve the task. Also included in the dataset is the information available to both users, the length of dialogue, and the speaker and âsystemâ interlocutor identities. | 1512.05742#103 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective. | http://arxiv.org/pdf/1512.05742 | Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau | cs.CL, cs.AI, cs.HC, cs.LG, stat.ML, 68T01, 68T05, 68T35, 68T50, I.2.6; I.2.7; I.2.1 | 56 pages including references and appendix, 5 tables and 1 figure;
Under review for the Dialogue & Discourse journal. Update: paper has been
rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
},
{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
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