We present a novel natural language generation system for spoken dialogue systems capable of entraining (adapting) to users' way of speaking, providing contextually appropriate responses. The generator is based on recurrent neural networks and the sequence-to-sequence approach. It is fully trainable from data which include preceding context along with responses to be generated. We show that the context-aware generator yields significant improvements over the baseline in both automatic metrics and a human pairwise preference test.
@cite_5: Syntactic priming effects, modelled as increase in repetition probability shortly after a use of a syntactic rule, have the potential to improve language processing components. We model priming of syntactic rules in annotated corpora of spoken dialogue, extending previous work that was confined to selected constructions. We find that speakers are more receptive to priming from their interlocutor in task-oriented dialogue than in spona-neous conversation. Low-frequency rules are more likely to show priming.
@cite_1: Alignment of interlocutors is a well known psycholinguistic phenomenon of great relevance for dialogue systems in general and natural language generation in particular. In this chapter, we present the alignment-capable microplanner SPUDprime. Using a priming-based model of interactive alignment, it is flexible enough to model the alignment behaviour of human speakers to a high degree. We demonstrate that SPUDprime can account for lexical as well as syntactic alignment and present an evaluation on corpora of task-oriented dialogue that were collected in two experiments designed to investigate the alignment behaviour of humans in a controlled fashion. This will allow for further investigation of which parameters are important to model alignment and how the human-computer interaction changes when the computer aligns to its users.
@cite_12: Human conversants in dialog adjust their behavior to their conversational partner in many ways. In terms of language use, they adapt to their partners both lexically and syntactically, by using the same referring expressions or sentence structure. In this paper, we describe a natural language generator personage-primed, which can produce utterances entrained to a range of utterance features used in prior utterances by a human user, and represented in the discourse context. Personage-primed can entrain to the user’s referring expressions, tense-modality selection, verb and noun lexical selection, hedge and cue word choice, and syntactic template selection, or any combination of these. To our knowledge, there is no other NLG engine that can dynamically generate all these types of entrainment in any utterance. We report an experiment testing all possible combinations of entrainment in a particular discourse context in order to test whether some types of entrainment are preferred, either because they make the utterance more natural, or because humans perceive the system as more friendly. Our experimental results suggest that human judgements of naturalness are distinct from friendliness: entraining on a user’s hedges increase perceptions of friendliness while reducing naturalness, while entraining on user’s referring expressions, syntactic template selection and tense modal choices increase perceptions of both naturalness and friendliness.
@cite_20: For a successful and satisfying interaction, a dialogue participant may align their language to be more like that of their interlocutor. In the first part of this paper, we examine the alignment phenomenon from the viewpoint of personalityrelated, linguistic, sociolinguistic and psycholinguistic research, concluding that some people are stronger aligners than others. Motivated by these results, we describe an approach to modelling alignment behaviour in a natural language generation system, using the OpenCCG surface realiser [30], which allows utterance candidates to be ranked by n-gram language models. We investigate the extent to which alignment can be simulated using word sequences alone (not syntactic structures). To this end, we interpolate a default language model with one calculated on the basis of a cached sentence. Experiments on sentences with the prepositional double object alternation show that varying the weight given to the cache model varies the propensity to align.
There have been several attempts at modelling entrainment in dialogue @cite_20 @cite_5 @cite_1 and even successful implementations of entrainment models in NLG systems for SDS, where entrainment caused an increase in perceived naturalness of the system responses @cite_12 or increased naturalness and task success (, lopes_automated_2013 ; lopes_rule-based_2015 ). However, all of the previous approaches are completely or partially rule-based. Most of them attempt to model entrainment explicitly, focus on specific entrainment phenomena only, and or require manually selected lists of variant expressions, while our system learns synonyms and entrainment rules implicitly from the corpus. A direct comparison with previous entrainment-capable NLG systems for SDS is not possible in our stand-alone setting since their rules involve the history of the whole dialogue whereas we focus on the preceding utterance in our experiments.