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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1512.05742 | 104 | The Verbmobil Corpus (Burger et al., 2000) is a multilingual corpus consisting of English, German, and Japanese dialogues collected for the purposes of training and testing the Verbmobil project system. The system was a designed for speech-to-speech machine translation tasks. Dia- logues were recorded in a variety of conditions and settings with room microphones, telephones, or close microphones, and were subsequently transcribed. Users were tasked with planning and scheduling an appointment throughout the course of the dialogue. Note that while there have been several versions of the Verbmobil corpora released, we refer to the entire collection here as described in (Burger et al., 2000). Dialogue acts were annotated in a subset of the corpus (1,505 mixed dia- logues in German, English and Japanese). 76,210 acts were annotated with 32 possible categories of dialogue acts Alexandersson et al. (2000)4.
Persuasion and Debates Another theme recurring among constrained spoken corpora is the ap- pearance of persuasion or debate tasks. These can involve general debates on a topic or tasking a speciï¬c interlocutor to try to convince another interlocutor of some opinion or topic. Generally,
4. Note, this information and further facts about the Verbmobil project and corpus can be found here: http: //verbmobil.dfki.de/facts.html
22 | 1512.05742#104 | 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 | 105 | 4. Note, this information and further facts about the Verbmobil project and corpus can be found here: http: //verbmobil.dfki.de/facts.html
22
these datasets record the outcome of how convinced the audience is of the argument at the end of the dialogue or debate.
The Green Persuasive Dataset (Douglas-Cowie et al., 2007) was recorded in 2007 to provide data for the HUMAINE project, whose goal is to develop interfaces that can register and respond to emotion. In the dataset, a persuader with strong pro-environmental (âpro-greenâ) feelings tries to convince persuadees to consider adopting more green lifestyles; these interactions are in the form of dialogues. It contains 8 long dialogues, totalling about 30 minutes each. Since the persuadees often either disagree or agree strongly with the persuaders points, this would be good corpus for studying social signs of (dis)-agreement between two people. | 1512.05742#105 | 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 | 106 | The MAHNOB Mimicry Database (Sun et al., 2011) contains 11 hours of recordings, split over 54 sessions between 60 people engaged either in a socio-political discussion or negotiating a tenancy agreement. This dataset consists of a set of fully synchronised audio-visual recordings of natural dyadic (one-on-one) interactions. It is one of several dialogue corpora that provide multi- modal data for analyzing human behaviour during conversations. Such corpora often consist of auditory, visual, and written transcriptions of the dialogues. Here, only audio-visual recordings are provided. The purpose of the dataset was to analyze mimicry (i.e. when one participant mimics the verbal and nonverbal expressions of their counterpart). The authors provide some benchmark video classiï¬cation models to this effect.
The Intelligence Squared Debate Dataset (Zhang et al., 2016) covers the âIntelligence Squaredâ Oxford-style debates taking place between 2006 and 2015. The topics of the debates vary across the dataset, but are constrained within the context of each debate. Speakers are labeled and the full transcript of the debate is provided. Furthermore, the outcome of the debate is provided (how many of the audience members were for the given proposal or against, before and after the debate). | 1512.05742#106 | 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"
},
{
"id": "1606.03632"
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1512.05742 | 107 | QA or Information Retrieval There are several corpora which feature direct question and an- swering sessions. These may involve general QA, such as in a press conference, or more task- speciï¬c lines of questioning, as to retrieve a speciï¬c set of information.
The Corpus of Professional Spoken American English (CPSAE) (Barlow, 2000) was con- structed using a selection of transcripts of interactions occurring in professional settings. The corpus contains two million words involving over 400 speakers, recorded between 1994-1998. The CPASE has two main components. The ï¬rst is a collection of transcripts (0.9 million words) of White House press conferences, which contains almost exclusively question and answer sessions, with some pol- icy statements by politicians. The second component consists of transcripts (1.1 million words) of faculty meetings and committee meetings related to national tests that involve statements, discus- sions, and questions. The creation of the corpus was motivated by the desire to understand and model more formal uses of the English language. | 1512.05742#107 | 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 | 108 | As previously mentioned, the Dialog State Tracking Challenge (DSTC) consists of a series of datasets evaluated using a âstate trackingâ or âslot ï¬llingâ metric. While the ï¬rst 3 installments of this challenge had conversations between a human participant and a computer, DSTC4 (Kim et al., 2015) contains dialogues between humans. In particular, this dataset has 35 conversations with 21 hours of interactions between tourists and tour guides over Skype, discussing information on hotels, ï¬ights, and car rentals. Due to the small size of the dataset, researchers were encouraged to use transfer learning from other datasets in the DSTC in order to improve state tracking performance. This same training set is used for DSTC5 (Kim et al., 2016) as well. However, the goal of DSTC5
23
is to study multi-lingual speech-act prediction, and therefore it combines the DSTC4 dialogues plus a set of equivalent Chinese dialogs; evaluation is done on a holdout set of Chinese dialogues.
Miscellaneous Lastly, there are several corpora which do not fall into any of the aforementioned categories, involving a range of tasks and situations. | 1512.05742#108 | 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 | 109 | Miscellaneous Lastly, there are several corpora which do not fall into any of the aforementioned categories, involving a range of tasks and situations.
The IDIAP Wolf Corpus (Hung and Chittaranjan, 2010) is an audio-visual corpus containing natural conversational data of volunteers who took part in an adversarial role-playing game called âWerewolfâ. Four groups of 8-12 people were recorded using headset microphones and synchro- nised video cameras, resulting in over 7 hours of conversational data. The novelty of this dataset is that the roles of other players are unknown to game participants, and some of the roles are decep- tive in nature. Thus, there is a signiï¬cant amount of lying that occurs during the game. Although speciï¬c instances of lying are not annotated, each speaker is labeled with their role in the game. In a dialogue setting, this could be useful for analyzing the differences in language when deception is being used. | 1512.05742#109 | 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 | 110 | The SEMAINE Corpus (McKeown et al., 2010) consists of 100 âemotionally colouredâ con- versations. Participants held conversations with an operator who adopted various roles designed to evoke emotional reactions. These conversations were recorded with synchronous video and audio devices. Importantly, the operatorsâ responses were stock phrases that were independent of the con- tent of the userâs utterances, and only dependent on the userâs emotional state. This corpus motivates building dialogue systems with affective and emotional intelligence abilities, since the corpus does not exhibit the natural language understanding that normally occurs between human interlocutors. The Loqui Human-Human Dialogue Corpus (Passonneau and Sachar, 2014) consists of an- notated transcriptions of telephone interactions between patrons and librarians at New York Cityâs Andrew Heiskell Braille & Talking Book Library in 2006. It stands out as it has annotated dis- cussion topics, question-answer pair links (adjacency pairs), dialogue acts, and frames (discourse units). | 1512.05742#110 | 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 | 111 | Similarly, the The ICSI Meeting Recorder Dialog Act (MRDA) Corpus (Shriberg et al., 2004) has annotated dialogue acts, question-answer pair links (adjacency pairs), and dialogue hot spots5. It consists of transcribed recordings of 75 ICSI meetings on several classes of topics including: the ICSI meeting recorder project itself, automatic speech recognition, natural language processing and neural theories of language, and discussions with the annotators for the project.
# 4.2.3 SCRIPTED CORPORA
A ï¬nal category of spoken dialogue consists of conversations that have been pre-scripted for the purpose of being spoken later. We refer to datasets containing such conversations as âscripted cor- poraâ. As discussed in Subsection 3.4, these datasets are distinct from spontaneous human-human conversations, as they inevitably contain fewer âï¬llerâ words and expressions that are common in spoken dialogue. However, they should not be confused with human-human written dialogues, as they are intended to sound like natural spoken conversations when read aloud by the participants. Furthermore, these scripted dialogues are required to be dramatic, as they are generally sourced from movies or TV shows. | 1512.05742#111 | 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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1512.05742 | 112 | There exist multiple scripted corpora based on movies and TV series. These can be sub-divided into two categories: corpora that provide the actual scripts (i.e. the movie script or TV series script) where each utterance is tagged with the appropriate speaker, and those that only contain subtitles
5. For more information on dialogue hot spots and how they relate to dialogue acts, see (Wrede and Shriberg, 2003).
24
and consecutive utterances are not divided or labeled in any way. It is always preferable to have the speaker labels, but there is signiï¬cantly more unlabeled subtitle data available, and both sources of information can be leveraged to build a dialogue system. | 1512.05742#112 | 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 | 113 | The Movie DiC Corpus (Banchs, 2012) is an example of the former caseâit contains about 130,000 dialogues and 6 million words from movie scripts extracted from the Internet Movie Script Data Collection6, carefully selected to cover a wide range of genres. These dialogues also come with context descriptions, as written in the script. One derivation based on this corpus is the Movie Triples Dataset (Serban et al., 2016). There is also the American Film Scripts Corpus and Film Scripts Online Corpus which form the Film Scripts Online Series Corpus, which can be pur- chased 7. The latter consists of a mix of British and American ï¬lm scripts, while the former consists of solely American ï¬lms. | 1512.05742#113 | 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"
},
{
"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 | 114 | The majority of these datasets consist mostly of raw scripts, which are not guaranteed to portray conversations between only two people. The dataset collected by Nio et al. (2014b), which we refer to as the Filtered Movie Script Corpus, takes over 1 million utterance-response pairs from web- based script resources and ï¬lters them down to 86,000 such pairs. The ï¬ltering method limits the extracted utterances to X-Y-X triples, where X is spoken by the same actor and each of the utterance share some semantic similarity. These triples are then decomposed into X-Y and Y-X pairs. Such ï¬ltering largely removes conversations with more than two speakers, which could be useful in some applications. Particularly, the ï¬ltering method helps to retain semantic context in the dialogue and keeps a back-and-forth conversational ï¬ow that is desired in training many dialogue systems. | 1512.05742#114 | 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 | 115 | The Cornell Movie-Dialogue Corpus (Danescu-Niculescu-Mizil and Lee, 2011) also has short conversations extracted from movie scripts. The distinguishing feature of this dataset is the amount of metadata available for each conversation: this includes movie metadata such as genre, release year, and IMDB rating, as well as character metadata such as gender and position on movie credits. Although this corpus contains 220,000 dialogue excerpts, it only contains 300,000 utterances; thus, many of the excerpts consist of single utterances.
The Corpus of American Soap Operas (Davies, 2012b) contains 100 million words in more than 22,000 transcripts of ten American TV-series soap operas from 2001 and 2012. Because it is based on soap operas it is qualitatively different from the Movie Dic Corpus, which contains movies in the action and horror genres. The corpus was collected to provide insights into colloquial Amer- ican speech, as the vocabulary usage is quite different from the British National Corpus (Davies, 2012a). Unfortunately, this corpus does not come with speaker labels. | 1512.05742#115 | 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 | 116 | Another corpus consisting of dialogues from TV shows is the TVD Corpus (Roy et al., 2014). This dataset consists of 191 movie transcripts from the comedy show The Big Bang Theory, and the drama show Game of Thrones, along with crowd-sourced text descriptions (brief episode sum- maries, longer episode outlines) and various types of metadata (speakers, shots, scenes). Text align- ment algorithms are used to link descriptions and metadata to the appropriate sections of each script. For example, one might align an event description with all the utterances associated with that event in order to develop algorithms for locating speciï¬c events from raw dialogue, such as âperson X tries to convince person Yâ.
Some work has been done in order to analyze character style from movie scripts. This is aided by a dataset collected by Walker et al. (2012a) that we refer to as the Character Style from Film Cor- pus. This corpus was collected from the IMSDb archive, and is annotated for linguistic structures
6. http://www.imsdb.com 7. http://alexanderstreet.com/products/film-scripts-online-series
25 | 1512.05742#116 | 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 | 117 | 6. http://www.imsdb.com 7. http://alexanderstreet.com/products/film-scripts-online-series
25
and character archetypes. Features, such as the sentiment behind the utterances, are automatically extracted and used to derive models of the characters in order to generate new utterances similar in style to those spoken by the character. Thus, this dataset could be useful for building dialogue personalization models.
There are two primary movie subtitle datasets: the OpenSubtitles (Tiedemann, 2012) and the SubTle Corpus (Ameixa and Coheur, 2013). Both corpora are based on the OpenSubtitles web- site.8 The OpenSubtitles dataset is a giant collection of movie subtitles, containing over 1 billion words, whereas SubTle Corpus has been pre-processed in order to extract interaction-response pairs that can help dialogue systems deal with out-of-domain (OOD) interactions. | 1512.05742#117 | 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 | 118 | The Corpus of English Dialogues 1560-1760 (CED) (Kyt¨o and Walker, 2006) compiles di- alogues from the mid-16th century until the mid-18th century. The sources vary from real trial transcripts to ï¬ction dialogues. Due to the scripted nature of ï¬ctional dialogues and the fact that the majority of the corpus consists of ï¬ctional dialogue, we classify it here as such. The corpus is com- posed as follows: trial proceedings (285,660 words), witness depositions (172,940 words), drama comedy works (238,590 words), didactic works (236,640 words), prose ï¬ction (223,890 words), and miscellaneous (25,970 words).
# 4.3 Human-Human Written Corpora
We proceed to survey corpora of conversations between humans in written form. As before, we sub-divide this section into spontaneous and constrained corpora, depending on whether there are restrictions on the topic of conversation. However, we make a further distinction between forum, micro-blogging, and chat corpora. | 1512.05742#118 | 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 | 119 | Forum corpora consist of conversations on forum-based websites such as Reddit9 where users can make posts, and other users can make comments or replies to said post. In some cases, com- ments can be nested indeï¬nitely, as users make replies to previous replies. Utterances in forum corpora tend to be longer, and there is no restriction on the number of participants in a discussion. On the other hand, conversations on micro-blogging websites such as Twitter10 tend to have very short utterances as there is an upper bound on the number of characters permitted in each message. As a result, these tend to exhibit highly colloquial language with many abbreviations. The identi- fying feature of chat corpora is that the conversations take place in real-time between users. Thus, these conversations share more similarities with spoken dialogue between humans, such as common grounding phenomena.
4.3.1 SPONTANEOUS WRITTEN CORPORA | 1512.05742#119 | 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 | 120 | 4.3.1 SPONTANEOUS WRITTEN CORPORA
We begin with written corpora where the topic of conversation is not pre-speciï¬ed. Such is the case for the NPS Internet Chatroom Conversations Corpus (Forsyth and Martell, 2007), which con- sists of 10,567 English utterances gathered from age-speciï¬c chat rooms of various online chat ser- vices from October and November of 2006. Each utterance was annotated with part-of-speech and dialogue act information; the correctness of this was veriï¬ed manually. The NPS Internet Chatroom Conversations Corpus was one of the ï¬rst corpora of computer-mediated communication (CMC),
8. http://www.opensubtitles.org 9. http://www.reddit.com 10. http://www.twitter.com
26
and it was intended for various NLP applications such as conversation thread topic detection, author proï¬ling, entity identiï¬cation, and social network analysis. | 1512.05742#120 | 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 | 121 | 26
and it was intended for various NLP applications such as conversation thread topic detection, author proï¬ling, entity identiï¬cation, and social network analysis.
Several corpora of spontaneous micro-blogging conversations have been collected, such as the Twitter Corpus from Ritter et al. (2010), which contains 1.3 million post-reply pairs extracted from Twitter. The corpus was originally constructed to aid in the production of unsupervised approaches to modeling dialogue acts. Larger Twitter corpora have been collected. The Twitter Triples Cor- pus (Sordoni et al., 2015b) is one such example, with a described original dataset of 127 million context-message-response triples, but only a small labeled subset of this corpus has been released. Speciï¬cally, the released labeled subset contains 4,232 pairs that scored an average of greater than 4 on the Likert scale by crowdsourced evaluators for quality of the response to the context-message pair. Similarly, large micro-blogging corpora such as the Sina Weibo Corpus (Shang et al., 2015), which contains 4.5 million post-reply pairs, have been collected; however, the authors have not yet been made publicly available. We do not include the Sina Weibo Corpus (and its derivatives) in the tables in this section, as they are not primarily in English. | 1512.05742#121 | 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 | 122 | The Usenet Corpus (Shaoul and Westbury, 2009) is a gigantic collection of public Usenet postings11 containing over 7 billion words from October 2005 to January 2011. Usenet was a distributed discussion system established in 1980 where participants could post articles to one of 47,860 ânewsgroupâ categories. It is seen as the precursor to many current Internet forums. The corpus derived from these posts has been used for research in collaborative ï¬ltering (Konstan et al., 1997) and role detection (Fisher et al., 2006).
The NUS SMS Corpus (Chen and Kan, 2013) consists of conversations carried out over mobile phone SMS messages between two users. While the original purpose of the dataset was to improve predictive text entry when mobile phones still mapped multiple letters to a single number, aided by video and timing analysis of users entering their messages it could equally be used for analysis of informal dialogue. Unfortunately, the corpus does not consist of dialogues, but rather single SMS messages. SMS messages are similar in style to Twitter, in that they use many abbreviations and acronyms. | 1512.05742#122 | 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 | 123 | Currently, one of the most popular forum-based websites is Reddit12 where users can create discussions and post comments in various sub-forums called âsubredditsâ. Each subreddit addresses its own particular topic. Over 1.7 billion of these comments have been collected in the Reddit Cor- pus.13 Each comment is labeled with the author, score (rating from other users), and position in the comment tree; the position is important as it determines which comment is being replied to. Al- though researchers have not yet investigated dialogue problems using this Reddit discussion corpus, the sheer size of the dataset renders it an interesting candidate for transfer learning. Additionally, researchers have used smaller collections of Reddit discussions for broad discourse classiï¬cation. (Schrading et al., 2015).
Some more curated versions of the Reddit dataset have been collected. The Reddit Domestic Abuse Corpus (Schrading et al., 2015) consists of Reddit posts and comments taken from either subreddits speciï¬c to domestic abuse, or from subreddits representing casual conversations, advice, and general anxiety or anger. The motivation is to build classiï¬ers that can detect occurrences of domestic abuse in other areas, which could provide insights into the prevalence and consequences | 1512.05742#123 | 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"
},
{
"id": "1610.02891"
},
{
"id": "1606.02689"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 124 | 11. http://www.usenet.net 12. http://www.reddit.com 13. https://www.reddit.com/r/datasets/comments/3bxlg7/i_have_every_publicly_
available_reddit_comment/
27
of these situations. These conversations have been pre-processed with lower-casing, lemmatizing, and removal of stopwords, and semantic role labels are provided.
# 4.3.2 CONSTRAINED WRITTEN CORPORA
There are also several written corpora where users are limited in terms of topics of conversation. For example, the Settlers of Catan Corpus (Afantenos et al., 2012) contains logs of 40 games of âSettlers of Catanâ, with about 80,000 total labeled utterances. The game is played with up to 4 players, and is predicated on trading certain goods between players. The goal of the game is to be the ï¬rst player to achieve a pre-speciï¬ed number of points. Therefore, the game is adversarial in nature, and can be used to analyze situations of strategic conversation where the agents have diverging motives. | 1512.05742#124 | 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 | 125 | Another corpus that deals with game playing is the Cards Corpus (Djalali et al., 2012), which consists of 1,266 transcripts of conversations between players playing a game in the âCards worldâ. This world is a simple 2-D environment where players collaborate to collect cards. The goal of the game is to collect six cards of a particular suit (cards in the environment are only visible to a player when they are near the location of that player), or to determine that this goal is impossible in the environment. The catch is that each player can only hold 3 cards, thus players must collaborate in order to achieve the goal. Further, each playerâs location is hidden to the other player, and there are a ï¬xed number of non-chatting moves. Thus, players must use the chat to formulate a plan, rather than exhaustively exploring the environment themselves. The dataset has been further annotated by Potts (2012) to collect all locative question-answer pairs (i.e. all questions of the form âWhere are you?â). | 1512.05742#125 | 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 | 126 | The Agreement by Create Debaters Corpus (Rosenthal and McKeown, 2015), the Agree- ment in Wikipedia Talk Pages Corpus (Andreas et al., 2012) and the Internet Argument Corpus (Abbott et al., 2016) all cover dialogs with annotations measuring levels of agreement or disagree- ment in responses to posts in various media. The Agreement by Create Debaters Corpus and the Agreement in Wikipedia Talk Pages Corpus both are formatted in the same way. Post-reply pairs are annotated with whether they are in agreement or disagreement, as well as the type of agreement they are in if applicable (e.g. paraphrasing). The difference between the two corpora is the source: the former is collected from Create Debate forums and the latter from a mix of Wikipedia Discus- sion pages and LiveJournal postings. The Internet Argument Corpus (IAC) (Walker et al., 2012b) is a forum-based corpus with 390,000 posts on 11,000 discussion topics. Each topic is controversial in nature, including subjects such as evolution, gay marriage and climate change; users participate by sharing their opinions on one of these topics. Posts-reply pairs have been labeled as being either in agreement or disagreement, and sarcasm ratings are given to each post. | 1512.05742#126 | 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 | 127 | Another source of constrained text-based corpora are chat-room environments. Such a set-up forms the basis of the MPC Corpus (Shaikh et al., 2010), which consists of 14 multi-party dialogue sessions of approximately 90 minutes each. In some cases, discussion topics were constrained to be about certain political stances, or mock committees for choosing job candidates. An interest- ing feature is that different participants are given different rolesâleader, disruptor, and consensus builderâwith only a general outline of their goals in the conversation. Thus, this dataset could be used to model social phenomena such as agenda control, inï¬uence, and leadership in on-line interactions.
28 | 1512.05742#127 | 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 | 128 | 28
The largest written corpus with a constrained topic is the recently released Ubuntu Dialogue Corpus (Lowe et al., 2015a), which has almost 1 million dialogues of 3 turns or more, and 100 It is related to the former Ubuntu Chat Corpus (Uthus and Aha, 2013). Both million words. corpora were scraped from the Ubuntu IRC channel logs.14 On this channel, users can log in and ask a question about a problem they are having with Ubuntu; these questions are answered by other users. Although the chat room allows everyone to chat with each other in a multi-party setting, the Ubuntu Dialogue Corpus uses a series of heuristics to disentangle it into dyadic dialogue. The technical nature and size of this corpus lends itself particularly well to applications in technical support.
Other corpora have been extracted from IRC chat logs. The IRC Corpus (Elsner and Charniak, 2008) contains approximately 50 hours of chat, with an estimated 20,000 utterances from the Linux channel on IRC, complete with the posting times. Therefore, this dataset consists of similarly technical conversations to the Ubuntu Corpus, with the occasional social chat. The purpose of this dataset was to investigate approaches for conversation disentanglement; given a multi-party chat room, one attempts to recover the individual conversations of which it is composed. For this purpose, there are approximately 1,500 utterances with annotated ground-truth conversations. | 1512.05742#128 | 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 | 129 | More recent efforts have combined traditional conversational corpora with question answering and recommendation datasets in order to facilitate the construction of goal-driven dialogue systems. Such is the case for the Movie Dialog Dataset (Dodge et al., 2015). There are four tasks that the authors propose as a prerequisite for a working dialogue system: question answering, recommenda- tion, question answering with recommendation, and casual conversation. The Movie Dialog dataset consists of four sub-datasets used for training models to complete these tasks: a QA dataset from the Open Movie Database (OMDb)15 of 116k examples with accompanying movie and actor metadata in the form of knowledge triples; a recommendation dataset from MovieLens16 with 110k users and 1M questions; a combined recommendation and QA dataset with 1M conversations of 6 turns each; and a discussion dataset from Redditâs movie subreddit. The former is evaluated using recall metrics in a manner similar to Lowe et al. (2015a). It should be noted that, other than the Reddit dataset, the dialogues in the sub-datasets are simulated QA pairs, where each response corresponds to a list of entities from the knowledge base.
# 5. Discussion
We conclude by discussing a number of general issues related to the development and evaluation of data-driven dialogue systems. We also discuss alternative sources of information, user personaliza- tion, and automatic evaluation methods. | 1512.05742#129 | 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 | 130 | # 5.1 Challenges of Learning from Large Datasets
Recently, several large-scale dialogue datasets have been proposed in order to train data-driven dialogue systems; the Twitter Corpus (Ritter et al., 2010) and the Ubuntu Dialogue corpus (Lowe et al., 2015a) are two examples. In this section, we discuss the beneï¬ts and drawbacks of these datasets based on our experience using them for building data-driven models. Unlike the previous
14. http://irclogs.ubuntu.com 15. http://en.omdb.org 16. http://movielens.org
29
section, we now focus explicitly on aspects of high relevance for using these datasets for learning dialogue strategies.
# 5.1.1 THE TWITTER CORPUS
The Twitter Corpus consists of a series of conversations extracted from tweets. While the dataset is large and general-purpose, the micro-blogging nature of the source material leads to several draw- backs for building conversational dialogue agents. However, some of these drawbacks do not apply if the end goal is to build an agent that interacts with users on the Twitter platform. | 1512.05742#130 | 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 | 131 | The Twitter Corpus has an enormous amount of typos, slang, and abbreviations. Due to the 140-character limit, tweets are often very short and compressed. In addition, users frequently use Twitter-speciï¬c devices such as hashtags. Unless one is building a dialogue agent speciï¬cally for Twitter, it is often not desirable to have a chatbot use hashtags and excessive abbreviations as it is not reï¬ective of how humans converse in other environments. This also results in a signiï¬cant increase in the word vocabulary required for dialogue systems trained at the word level. As such, it is not surprising that character-level models have shown promising results on Twitter (Dhingra et al., 2016).
Twitter conversations often contain various kinds of verbal role-playing and imaginative actions similar to stage directions in theater plays (e.g. instead of writing âgoodbyeâ, a user might write â*waves goodbye and leaves*â). These conversations are very different from the majority of text- based chats. Therefore, dialogue models trained on this dataset are often able to provide interesting and accurate responses to contexts involving role-playing and imaginative actions (Serban et al., 2017b). | 1512.05742#131 | 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 | 132 | Another challenge posed by Twitter is that Twitter conversations often refer to recent public events outside the conversation. In order to learn effective responses for such conversations, a dialogue agent must infer the news event under discussion by referencing some form of external knowledge base. This would appear to be a particularly difï¬cult task.
# 5.1.2 THE UBUNTU DIALOGUE CORPUS
The Ubuntu Dialogue Corpus is one of the largest, publicly available datasets containing technical support dialogues. Due to the commercial importance of such systems, the dataset has attracted signiï¬cant attention.17 Thus, the Ubuntu Dialogue Corpus presents the opportunity for anyone to train large-scale data-driven technical support dialogue systems. | 1512.05742#132 | 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": "1610.02891"
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"id": "1606.02689"
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{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 133 | Despite this, there are several problems when training data-driven dialogue models on the Ubuntu Dialogue Corpus due to the nature of the data. First, since the corpus comes from a multi- party IRC channel, it needs to be disentangled into separate dialogues. This disentanglement process is noisy, and errors inevitably arise. The most frequent error is when a missing utterance in the di- alogue is not picked up by the extraction procedure (e.g. an utterance from the original multi-party chat was not added to the disentangled dialogue). As a result, for a substantial amount of conver- sations, it is difï¬cult to follow the topic. In particular, this means that some of the Next Utterance Classiï¬cation (NUC) examples, where models must select the correct next response from a list of candidates, are either difï¬cult or impossible for models to predict.
17. Most of the largest technical support datasets are based on commercial technical support channels, which are propri- etary and never released to the public for privacy reasons.
30 | 1512.05742#133 | 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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"id": "1606.02689"
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"id": "1606.01269"
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"id": "1506.05869"
}
] |
1512.05742 | 134 | 17. Most of the largest technical support datasets are based on commercial technical support channels, which are propri- etary and never released to the public for privacy reasons.
30
Another problem arises from the lack of annotations and labels. Since users try to solve their technical problems, it is perhaps best to build models under a goal-driven dialogue framework, where a dialogue system has to maximize the probability that it will solve the userâs problem at the end of the conversation. However, there are no reward labels available. Thus, it is difï¬cult to model the dataset in a goal-driven dialogue framework. Future work may alleviate this by constructing automatic methods of determining whether a user in a particular conversation solved their problem.
A particular challenge of the Ubuntu Dialogue Corpus is the large number of out-of-vocabulary words, including many technical words related to the Ubuntu operating system, such as commands, software packages, websites, etc. Since these words occur rarely in the dataset, it is difï¬cult to learn their meaning directly from the dataset â for example, it is difï¬cult to obtain meaningful distributed, real-valued vector representations for neural network-based dialogue models. This is further exacerbated by the large number of users who use different nomenclature, acronyms, and speaking styles, and the many typos in the dataset. Thus, the linguistic diversity of the corpus is large. | 1512.05742#134 | 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"
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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 | 135 | A ï¬nal challenge of the dataset is the necessity for additional knowledge related to Ubuntu in order to accurately generate or predict the next response in a conversation. We hypothesize that this knowledge is crucial for a system trained on the Ubuntu Dialogue Corpus to be effective in practice, as often solutions to technical problems change over time as new versions of the operating system become available. Thus, an effective dialogue system must learn to combine up-to-date technical information with an understanding of natural language dialogue in order to solve the usersâ problems. We will discuss the use of external knowledge in more detail in Section 5.5.
While these challenges make it difï¬cult to build data-driven dialogue systems, it also presents an important research opportunity. Current data-driven dialogue systems perform rather poorly in terms of generating utterances that are coherent and on-topic (Serban et al., 2017a). As such, there is signiï¬cant room for improvement on these models.
# 5.2 Transfer Learning Between Datasets
While it is not always feasible to obtain large corpora for every new application, the use of other related datasets can effectively bootstrap the learning process. In several branches of machine learn- ing, and in particular in deep learning, the use of related datasets in pre-training the model is an effective method of scaling up to complex environments (Erhan et al., 2010; Kumar et al., 2015). | 1512.05742#135 | 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": "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"
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] |
1512.05742 | 136 | To build open-domain dialogue systems, it is arguably necessary to move beyond domain- speciï¬c datasets. Instead, like humans, dialogue systems may have to be trained on multiple data sources for solving multiple tasks. To leverage statistical efï¬ciency, it may be necessary to ï¬rst use unsupervised learningâas opposed to supervised learning or ofï¬ine reinforcement learning, which typically only provide a sparse scalar feedback signal for each phrase or sequence of phrasesâand then ï¬ne-tune models based on human feedback. Researchers have already proposed various ways of applying transfer learning to build data-driven dialogue systems, ranging from learning separate sub-components of the dialogue system (e.g. intent and dialogue act classiï¬cation) to learning the entire dialogue system (e.g. in an unsupervised or reinforcement learning framework) using transfer learning (Fabbrizio et al., 2004; Forgues et al., 2014; Serban and Pineau, 2015; Serban et al., 2016; Lowe et al., 2015a; Vandyke et al., 2015; Wen et al., 2016; GaËsi´c et al., 2016; Mo et al., 2016; Genevay and Laroche, 2016; Chen et al., 2016)
31
# 5.3 Topic-oriented & Goal-driven Datasets | 1512.05742#136 | 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": "1511.08099"
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"id": "1604.06045"
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"id": "1606.02689"
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"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 137 | 31
# 5.3 Topic-oriented & Goal-driven Datasets
Tables 1â5 list the topics of available datasets. Several of the human-human datasets are denoted as having casual or unrestricted topics. In contrast, most human-machine datasets focus on speciï¬c, narrow topics. It is useful to keep this distinction between restricted and unrestricted topics in mind, as goal-driven dialogue systems â which typically have a well-deï¬ned measure of performance related to task completion â are usually developed in the former setting. In some cases, the line between these two types of datasets blurs. For example, in the case of conversations occurring between players of an online game (Afantenos et al., 2012), the outcome of the game is determined by how participants play in the game environment, not by their conversation. In this case, some conversations may have a direct impact on a playerâs performance in the game, some conversations may be related to the game but irrelevant to the goal (e.g. commentary on past events) and some conversations may be completly unrelated to the game.
# 5.4 Incorporating longer memories | 1512.05742#137 | 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"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 138 | # 5.4 Incorporating longer memories
Recently, signiï¬cant progress has been made towards incorporating a form of external memory into various neural-network architectures for sequence modeling. Models such as Memory Net- works (Weston et al., 2015; Sukhbaatar et al., 2015) and Neural Turing Machines (NTM) (Graves et al., 2014) store some part of their input in a memory, which is then reasoned over in order to perform a variety of sequence to sequence tasks. These vary from simple problems, such as se- quence copying, to more complex problems, such as question answering and machine translation. Although none of these models are explicitly designed to address dialogue problems, the extension by Kumar et al. (2015) to Dynamic Memory Networks speciï¬cally differentiates between episodic and semantic memory. In this case, the episodic memory is the same as the memory used in the traditional Memory Networks paper that is extracted from the input, while the semantic memory refers to knowledge sources that are ï¬xed for all inputs. The model is shown to work for a variety of NLP tasks, and it is not difï¬cult to envision an application to dialogue utterance generation where the semantic memory is the desired external knowledge source.
# 5.5 Incorporating External Knowledge | 1512.05742#138 | 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"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 139 | # 5.5 Incorporating External Knowledge
Another interesting research direction is the incorporation of external knowledge sources in order to inform the response to be generated. Using external information is of great importance to dialogues systems, particularly in the goal-driven setting. Even non-goal-driven dialogue systems designed to simply entertain the user could beneï¬t from leveraging external information, such as current news articles or movie reviews, in order to better converse about real-world events. This may be particularly useful in data-sparse domains, where there is not enough dialogue training data to reliably learn a response that is appropriate for each input utterance, or in domains that evolve quickly over time.
# 5.5.1 STRUCTURED EXTERNAL KNOWLEDGE
In traditional goal-driven dialogue systems (Levin and Pieraccini, 1997), where the goal is to provide information to the user, there is already extensive use of external knowledge sources. For example, in the Letâs Go! dialogue system (Raux et al., 2005), the user requests information about various bus arrival and departure times. Thus, a critical input to the model is the actual bus schedule, which is
32 | 1512.05742#139 | 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": "1605.03481"
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"id": "1506.05869"
}
] |
1512.05742 | 140 | 32
used in order to generate the systemâs utterances. Another example is the dialogue system described by N¨oth et al. (2004), which helps users ï¬nd movie information by utilizing movie showtimes from different cinemas. Such examples are abundant both in the literature and in practice. Although these models make use of external knowledge, the knowledge sources in these cases are highly structured and are only used to place hard constraints on the possible states of an utterance to be generated. They are essentially contained in relational databases or structured ontologies, and are only used to provide a deterministic mapping from the dialogue states extracted from an input user utterance to the dialogue system state or the generated response. | 1512.05742#140 | 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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] |
1512.05742 | 141 | Complementary to domain-speciï¬c databases and ontologies are the general natural language processing databases and tools. These include lexical databases such as WordNet (Miller, 1995), which contains lexical relationships between words for over a hundred thousand words, VerbNet (Schuler, 2005) which contains lexical relations between verbs, and FrameNet (Ruppenhofer et al., 2006), which contains âword sensesâ for over ten thousand words along with examples of each word sense. In addition, there exist several natural language processing tools such as part of speech taggers, word category classiï¬ers, word embedding models, named entity recognition models, co- reference resolution models, semantic role labeling models, semantic similarity models and sen- timent analysis models (Manning and Sch¨utze, 1999; Jurafsky and Martin, 2008; Mikolov et al., 2013; Gurevych and Strube, 2004; Lin and Walker, 2011b) that may be used by the Natural Lan- guage Interpreter to extract meaning from human utterances. Since these tools are typically built upon texts and annotations created by humans, using them inside a dialogue system can be inter- preted as a form of structured transfer learning, where the relationships or labels learned from the original natural language processing corpus provide additional information to the dialogue system and improve generalization of the system. | 1512.05742#141 | 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": "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 | 142 | # 5.5.2 UNSTRUCTURED EXTERNAL KNOWLEDGE
Complementary sources of information can be found in unstructured knowledge sources, such as online encyclopedias (Wikipedia (Denoyer and Gallinari, 2007)) as well as domain-speciï¬c It is beyond the scope of this paper to review all possible ways sources (Lowe et al., 2015b). that these unstructured knowledge sources have or could be used in conjunction with a data-driven dialogue system. However, we note that this is likely to be a fruitful research area.
# 5.6 Personalized dialogue agents | 1512.05742#142 | 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": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 143 | # 5.6 Personalized dialogue agents
When conversing, humans often adapt to their interlocutor to facilitate understanding, and thus improve conversational efï¬ciency and satisfaction. Attaining human-level performance with dia- logue agents may well require personalization, i.e. models that are aware and capable of adapting to their intelocutor. Such capabilities could increase the effectiveness and naturalness of generated dialogues (Lucas et al., 2009; Su et al., 2013). We see personalization of dialogue systems as an important task, which so far has not received much attention. There has been initial efforts on user- speciï¬c models which could be adapted to work in combination with the dialogue models presented in this survey (Lucas et al., 2009; Lin and Walker, 2011a; Pargellis et al., 2004). There has also been interesting work on character modeling in movies (Walker et al., 2011; Li et al., 2016; Mo et al., 2016). There is signiï¬cant potential to learn user models as part of dialogue models. The large datasets presented in this paper, some of which provide multiple dialogues per user, may enable the development of such models.
33
# 5.7 Evaluation metrics | 1512.05742#143 | 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"
},
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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"
},
{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 144 | 33
# 5.7 Evaluation metrics
One of the most challenging aspects of constructing dialogue systems lies in their evaluation. While the end goal is to deploy the dialogue system in an application setting and receive real human feed- back, getting to this stage is time consuming and expensive. Often it is also necessary to optimize performance on a pseudo-performance metric prior to release. This is particularly true if a dialogue model has many hyper-parameters to be optimizedâit is infeasible to run user experiments for every parameter setting in a grid search. Although crowdsourcing platforms, such as Amazon Mechanical Turk, can be used for some user testing (Jurcıcek et al., 2011), evaluations using paid subjects can also lead to biased results (Young et al., 2013). Ideally, we would have some automated metrics for calculating a score for each model, and only involve human evaluators once the best model has been chosen with reasonable conï¬dence. | 1512.05742#144 | 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"
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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"
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] |
1512.05742 | 145 | The evaluation problem also arises for non-goal-driven dialogue systems. Here, researchers have focused mainly on the output of the response generation module. Evaluation of such non-goal- driven dialogue systems can be traced back to the Turing test (Turing, 1950), where human judges communicate with both computer programs and other humans over a chat terminal without knowing each otherâs true identity. The goal of the judges was to identify the humans and computer programs under the assumption that a program indistinguishable from a real human being must be intelligent. However, this setup has been criticized extensively with numerous researchers proposing alterna- tive evaluation procedures (Cohen, 2005). More recently, researchers have turned to analyzing the collected dialogues produced after they are ï¬nished (Galley et al., 2015; Pietquin and Hastie, 2013; Shawar and Atwell, 2007a; Schatzmann et al., 2005). | 1512.05742#145 | 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 | 146 | Even when human evaluators are available, it is often difï¬cult to choose a set of informative and consistent criteria that can be used to judge an utterance generated by a dialogue system. For example, one might ask the evaluator to rate the utterance on vague notions such as âappropriatenessâ and ânaturalnessâ, or to try to differentiate between utterances generated by the system and those generated by actual humans (Vinyals and Le, 2015). Schatzmann et al. (2005) suggest two aspects that need to be evaluated for all response generation systems (as well as user simulation models): 1) if the model can generate human-like output, and 2) if the model can reproduce the variety of user behaviour found in corpus. But we lack a deï¬nitive framework for such evaluations.
We complete this discussion by summarizing different approaches to the automatic evaluation problem as they relate to these objectives.
5.7.1 AUTOMATIC EVALUATION METRICS FOR GOAL-DRIVEN DIALOGUE SYSTEMS | 1512.05742#146 | 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 | 147 | 5.7.1 AUTOMATIC EVALUATION METRICS FOR GOAL-DRIVEN DIALOGUE SYSTEMS
User evaluation of goal-driven dialogue systems typically focuses on goal-related performance cri- teria, such as goal completion rate, dialogue length, and user satisfaction (Walker et al., 1997; Schatzmann et al., 2005). These were originally evaluated by human users interacting with the dialogue system, but more recently researchers have also begun to use third-party annotators for evaluating recorded dialogues (Yang et al., 2010). Due to their simplicity, the vast majority of hand- crafted task-oriented dialogue systems have been solely evaluated in this way. However, when using machine learning algorithms to train on large-scale corpora, automatic optimization criteria are re- quired. The challenge with evaluating goal-driven dialogue systems without human intervention is that the process necessarily requires multiple stepsâit is difï¬cult to determine if a task has been solved from a single utterance-response pair from a conversation. Thus, simulated data is often gen- erated by a user simulator (Eckert et al., 1997; Schatzmann et al., 2007; Jung et al., 2009; Georgila
34 | 1512.05742#147 | 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": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 148 | 34
et al., 2006; Pietquin and Hastie, 2013). Given a sufï¬ciently accurate user simulation model, an interaction between the dialogue system and the user can be simulated from which it is possible to deduce the desired metrics, such as goal completion rate. Signiï¬cant effort has been made to render the simulated data as realistic as possible, by modeling user intentions. Evaluation of such simulation methods has already been conducted (Schatzmann et al., 2005). However, generating realistic user simulation models remains an open problem.
5.7.2 AUTOMATIC EVALUATION METRICS FOR NON-GOAL-DRIVEN DIALOGUE SYSTEMS
Evaluation of non-goal-driven dialogue systems, whether by automatic means or user studies, re- mains a difï¬cult challenge. | 1512.05742#148 | 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"
},
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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"
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{
"id": "1506.05869"
}
] |
1512.05742 | 149 | Word Overlap Metrics. One approach is to borrow evaluation metrics from other NLP tasks such as machine translation, which uses BLEU (Papineni et al., 2002) and METEOR (Banerjee and Lavie, 2005) scores. These metrics have been used to compare responses generated by a learned dialogue strategy to the actual next utterance in the conversation, conditioned on a dialogue context (Sordoni et al., 2015b). While BLEU scores have been shown to correlate with human judgements for machine translation (Papineni et al., 2002), their effectiveness for automatically assessing di- alogue response generation is unclear. There are several issues to consider: given the context of a conversation, there often exists a large number of possible responses that âï¬tâ into the dialogue. Thus, the response generated by a dialogue system could be entirely reasonable, yet it may have no words in common with the actual next utterance. In this case, the BLEU score would be very low, but would not accurately reï¬ect the strength of the model. Indeed, even humans who are tasked with predicting the next utterance of a conversation achieve relatively low BLEU scores (Sordoni et al., | 1512.05742#149 | 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"
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{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 150 | the model. Indeed, even humans who are tasked with predicting the next utterance of a conversation achieve relatively low BLEU scores (Sordoni et al., 2015b). Although the METEOR metric takes into account synonyms and morphological variants of words in the candidate response, it still suffers from the aforementioned problems. In a sense, these measurements only satisfy one direction of Schatzmannâs criteria: high BLEU and METEOR scores imply that the model is generating human-like output, but the model may still not reproduce the variety of user behaviour found in corpus. Furthermore, such metrics will only accurately re- ï¬ect the performance of the dialogue system if given a large number of candidate responses for each given context. | 1512.05742#150 | 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 | 151 | Next Utterance Classiï¬cation. Alternatively, one can narrow the number of possible responses to a small, pre-deï¬ned list, and ask the model to select the most appropriate response from this list. The list includes the actual next response of the conversation (the desired prediction), and the other entries (false positives) are sampled from elsewhere in the corpus (Lowe et al., 2016, 2015a). This next utterance classiï¬cation (NUC) task is derived from the recall and precision metrics for information-retrieval-based approaches. There are several attractive properties of this metric: it is easy to interpret, and the difï¬culty can be adjusted by changing the number of false responses. However, there are drawbacks. In particular, since the other candidate answers are sampled from elsewhere in the corpus, there is a chance that these also represent reasonable responses given the context. This can be alleviated to some extent by reporting Recall@k measures, i.e. whether the correct response is found in the k responses with the highest rankings according to the model. Although current models evaluated using NUC are trained explicitly to maximize the performance on this metric by minimizing the cross-entropy between context-response pairs (Lowe et al., 2015a; Kadlec et al., 2015), the metric could also be used to evaluate a probabilistic generative model trained to output full utterances. | 1512.05742#151 | 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"
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{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
1512.05742 | 152 | 35
Word Perplexity. Another metric proposed to evaluate probabilistic language models (Bengio et al., 2003; Mikolov et al., 2010) that has seen signiï¬cant recent use for evaluating end-to-end dialogue systems is word perplexity (Pietquin and Hastie, 2013; Serban et al., 2016). Perplexity ex- plicitly measures the probability that the model will generate the ground truth next utterance given some context of the conversation. This is particularly appealing for dialogue, as the distribution over words in the next utterance can be highly multi-modal (i.e. many possible responses). A re-weighted perplexity metric has also been proposed where stop-words, punctuation, and end-of-utterance to- kens are removed before evaluating to focus on the semantic content of the phrase (Serban et al., 2016). Both word perplexity, as well as utterance-level recall and precision outlined above, satisfy Schatzmannâs evaluation criteria, since scoring high on these would require the model to produce human-like output and to reproduce most types of conversations in the corpus. | 1512.05742#152 | 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 | 153 | Response Diversity. Recent non-goal-driven dialogue systems based on neural networks have had problems generating diverse responses (Serban et al., 2016). (Li et al., 2015) recently intro- duced two new metrics, distinct-1 and distinct-2, which respectively measure the number of distinct unigrams and bigrams of the generated responses. Although these fail to satisfy either of Schatz- mannâs criteria, they may still be useful in combination with other metrics, such as BLEU, NUC or word perplexity.
# 6. Conclusion
There is strong evidence that over the next few years, dialogue research will quickly move towards large-scale data-driven model approaches. In particular, as is the case for other language-related applications such as speech recognition, machine translation and information retrieval, these ap- proaches will likely come in the form of end-to-end trainable systems. This paper provides an extensive survey of currently available datasets suitable for research, development, and evaluation of such data-driven dialogue systems.
In addition to presenting the datasets, we provide a detailed discussion of several of the is- sues related to the use of datasets in dialogue system research. Several potential directions are highlighted, such as transfer learning and incorporation of external knowledge, which may lead to scalable solutions for end-to-end training of conversational agents.
# Acknowledgements | 1512.05742#153 | 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 | 154 | # Acknowledgements
The authors gratefully acknowledge ï¬nancial support by the Samsung Advanced Institute of Tech- nology (SAIT), the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada Research Chairs, the Canadian Institute for Advanced Research (CIFAR) and Compute Canada. Early versions of the manuscript beneï¬ted greatly from the proofreading of Melanie Lyman-Abramovitch, and later versions were extensively revised by Genevieve Fried and Nico- las Angelard-Gontier. The authors also thank Nissan Pow, Michael Noseworthy, Chia-Wei Liu, Gabriel Forgues, Alessandro Sordoni, Yoshua Bengio and Aaron Courville for helpful discussions.
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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,
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the area of dialogue systems, the trend is less obvious, and most practical
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data-driven learning of dialogue systems. We discuss important characteristics
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data-driven learning of dialogue systems. We discuss important characteristics
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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
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},
{
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systems are still built through significant engineering and expert knowledge.
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data-driven learning of dialogue systems. We discuss important characteristics
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},
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},
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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 | [
{
"id": "1511.06931"
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"id": "1606.03632"
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"id": "1611.09268"
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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"
}
] |
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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 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
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{
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{
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{
"id": "1511.08099"
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{
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"id": "1610.02891"
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"id": "1606.02689"
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{
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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 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
"id": "1607.00070"
},
{
"id": "1503.02364"
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{
"id": "1511.08099"
},
{
"id": "1604.06045"
},
{
"id": "1610.02891"
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{
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{
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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"
},
{
"id": "1606.03632"
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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 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
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},
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},
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},
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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 | [
{
"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"
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"id": "1605.03481"
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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 | [
{
"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"
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{
"id": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
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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 | [
{
"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"
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{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
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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 | [
{
"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"
},
{
"id": "1506.05869"
}
] |
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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 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
"id": "1611.09268"
},
{
"id": "1510.03055"
},
{
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},
{
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{
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{
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{
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{
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}
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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 | [
{
"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": "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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}
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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 | [
{
"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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}
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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 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
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},
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},
{
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},
{
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},
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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;
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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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{
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},
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},
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},
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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;
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rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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{
"id": "1606.03632"
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},
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},
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},
{
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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 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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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 | [
{
"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"
},
{
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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 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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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 | [
{
"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"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
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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;
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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;
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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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},
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{
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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;
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rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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{
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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
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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
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and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
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{
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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;
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{
"id": "1511.06931"
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{
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systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
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data-driven learning of dialogue systems. We discuss important characteristics
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the area of dialogue systems, the trend is less obvious, and most practical
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Nevertheless, several recent results suggest that data-driven approaches are
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data-driven learning of dialogue systems. We discuss important characteristics
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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": "1606.01269"
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{
"id": "1605.03481"
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{
"id": "1506.05869"
}
] |
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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 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
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},
{
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},
{
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{
"id": "1503.02364"
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{
"id": "1511.08099"
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{
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{
"id": "1610.02891"
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{
"id": "1606.02689"
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{
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}
] |
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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;
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rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
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},
{
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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 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
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},
{
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},
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},
{
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{
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{
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}
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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;
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rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
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},
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},
{
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}
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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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},
{
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},
{
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},
{
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},
{
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{
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{
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},
{
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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;
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rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
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{
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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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] |
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the area of dialogue systems, the trend is less obvious, and most practical
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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
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and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
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rewritten and now includes several new datasets | null | cs.CL | 20151217 | 20170321 | [
{
"id": "1511.06931"
},
{
"id": "1606.03632"
},
{
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{
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},
{
"id": "1607.00070"
},
{
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},
{
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},
{
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},
{
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{
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{
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] |
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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 | [
{
"id": "1511.06931"
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"id": "1606.03632"
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"id": "1611.09268"
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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": "1606.01269"
},
{
"id": "1605.03481"
},
{
"id": "1506.05869"
}
] |
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T.-H. Wen, M. Gasic, N. Mrksic, L. M. Rojas-Barahona, P.-H. Su, D. Vandyke, and S. Young. Multi-domain neural In Conference of the North American Chapter of the network language generation for spoken dialogue systems. Association for Computational Linguistics (NAACL-HLT 2016), 2016.
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In International Conference on Learning Representations (ICLR), 2015. | 1512.05742#199 | 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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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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45
# Appendix A. Learning from Dialogue Corpora | 1512.05742#201 | 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 | 202 | 45
# Appendix A. Learning from Dialogue Corpora
In this appendix section, we review some existing computational architectures suitable for learning dialogue strategies directly from data. The goal is not to provide full technical details on the methods available to achieve this â though we provide appropriate citations for the interested reader â but rather to illustrate concretely how the datasets described above can, and have, been used in different dialogue learning efforts. As such, we limit this review to a small set of existing work.
# A.1 Data Pre-processing
Before applying machine learning methods to a dialogue corpus, it is common practice to perform some form of pre-processing. The aim of pre-processing is to standardize a dataset with minimal loss of information. This can reduce data scarcity, and eventually make it easier for models to learn from the dataset. In natural language processing, it is commonly acknowledged that pre-processing can have a signiï¬cant effect on the results of the natural language processing systemâthe same observation holds for dialogue. Although the speciï¬c procedure for pre-processing is task- and data-dependent, in this section we highlight a few common approaches, in order to give a general idea of where pre-processing can be effective for dialogue systems. | 1512.05742#202 | 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 | 203 | Pre-processing is often used to remove anomalies in the data. For text-based corpora, this can include removing acronyms, slang, misspellings and phonemicization (e.g. where words are written according to their pronunciation instead of their correct spelling). For some models, such as the generative dialogue models discussed later, tokenization (e.g. deï¬ning the smallest unit of input) is also critical. In datasets collected from mobile text, forum, microblog and chat-based settings, it is common to observe a signiï¬cant number of acronyms, abbreviations, and phonemicizations that are speciï¬c to the topic and userbase (Clark, 2003). Although there is no widely accepted standard for handling such occurrences, many NLP systems incorporate some form of pre-processing to normalize these entries (Kaufmann and Kalita, 2010; Aw et al., 2006; Clark, 2003). For example, there are look-up tables, such as the IRC Beginner List18, which can be used to translate the most common acronyms and slang into standard English. Another common strategy is to use stemming and lemmatization to replace many words with a single item | 1512.05742#203 | 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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