A Generative Model for User Simulation in a Spatial Navigation Domain

Eshky, Aciel; Allison, Ben; Ramamoorthy, Subramanian; Steedman, Mark

doi:10.3115/v1/e14-1066

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Its behaviour is often unreasonable since it only takes the latest system action as input without any information about the user goal. Therefore, models which can act on a given user goal were introduced (Georgila et al, 2006;Eshky et al, 2012). A Bayesian user simulation model which predicts the user action based on the user goal is proposed by Daubigney et al (2012).…”

Section: Related Workmentioning

confidence: 99%

GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers

Lin,

Geishauser,

Feng

et al. 2022

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

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User simulators (USs) are commonly used to train task-oriented dialogue systems (DSs) via reinforcement learning. The interactions often take place on semantic level for efficiency, but there is still a gap from semantic actions to natural language, which causes a mismatch between training and deployment environment. Incorporating a natural language generation (NLG) module with USs during training can partly deal with this problem. However, since the policy and NLG of USs are optimised separately, these simulated user utterances may not be natural enough in a given context. In this work, we propose a generative transformerbased user simulator (GenTUS). GenTUS consists of an encoder-decoder structure, which means it can optimise both the user policy and natural language generation jointly. GenTUS generates both semantic actions and natural language utterances, preserving interpretability and enhancing language variation. In addition, by representing the inputs and outputs as word sequences and by using a large pre-trained language model we can achieve generalisability in feature representation. We evaluate Gen-TUS with automatic metrics and human evaluation. Our results show that GenTUS generates more natural language and is able to transfer to an unseen ontology in a zero-shot fashion. In addition, its behaviour can be further shaped with reinforcement learning opening the door to training specialised user simulators.

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Section: Related Workmentioning

confidence: 99%

GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers

Lin,

Geishauser,

Feng

et al. 2022

Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

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“…Since it only has access to the latest system action, its behaviour can be illogical if the goal changes. Therefore, models which can take into account a given user goal were introduced (Georgila et al, 2006;Eshky et al, 2012). The Bayesian model of predicts the user action based on the user goal, and hidden Markov models are used to model the user and the system behaviour (Cuayáhuitl et al, 2005).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

2021

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We are glad to pen the first few words for the proceedings of SIGDIAL 2021, the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue. The SIGDIAL conference is a premier publication venue for research in discourse and dialogue. This year, the conference is organized as a hybrid event with both in-person and virtual participation on July 29-31, 2021, right before ACL-IJCNLP 2021. The 2021 Young Researchers' Roundtable on Spoken Dialog Systems (YRRSDS 2021) is also held as a satellite event. The SIGDIAL 2021 program features three keynote talks, 6 paper presentation sessions, 1 demo session, and 2 special sessions, entitled "Summarization of Dialogues and Multi-Party Meetings", and "Safety for E2E Conversational AI".COVID has changed the way we work, but it doesn't hamper our research progress. We received 142 submissions this year, comprising 88 long papers, 49 short papers, and 5 demo descriptions. We had 12 Senior Program Committee (SPC) members who were each responsible for 11-12 papers, leading the discussion process and also contributing meta-reviews. Each submission was assigned to an SPC member and received at least three reviews. Decisions carefully considered the original reviews, meta-reviews, and discussions among reviewers facilitated by the SPCs. We are immensely grateful to the members of the Program Committee and Senior Program Committee for efforts in providing excellent, thoughtful reviews of the large number of submissions. Their contributions have been essential to selecting the accepted papers and providing a high-quality technical program for the conference. We have aimed to develop a broad, varied program spanning the many positively rated papers identified by the review process. We accepted 59 papers in total: 40 long papers (45%), 15 short papers (31%), and 4 demo descriptions, for an overall acceptance rate of 41.5%, in line with prior years. One keynote will highlight each of the three days of the conference. In organizing this hybrid inperson/virtual conference, we have tried to maintain as much of the spirit of a fully online conference as possible. Recordings for all papers and demos have been made available several days before the start of the conference, for participants to watch asynchronously. Long and short papers are organized into sessions taking into consideration the presenters' time zones. Regular papers sessions span 8-11 papers, each presented as a two-minute pre-recorded talk followed by five minutes of live Q&A. For demos, we organized four parallel zoom rooms to allow participants to interact with and observe live interactions with the systems. The topics represent the breadth of research in discourse and dialogue. A conference of this size requires the energy, guidance, and contributions of many parties, and we would like to take this opportunity to thank and acknowledge them all. We thank our three keynote speakers, Julia Hirschberg (Columbia University), Raymond J. Mooney (University of Texas at Austin), and Jason Weston (Facebook AI & NYU), for...

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“…Since it only has access to the latest system action, its behaviour can be illogical if the goal changes. Therefore, models which can take into account a given user goal were introduced (Georgila et al, 2006;Eshky et al, 2012). The Bayesian model of Daubigney et al (2012) predicts the user action based on the user goal, and hidden Markov models are used to model the user and the system behaviour (Cuayáhuitl et al, 2005).…”

Section: Related Workmentioning

confidence: 99%

Domain-independent User Simulation with Transformers for Task-oriented Dialogue Systems

Lin,

Lubis,

et al. 2021

Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Dialogue policy optimisation via reinforcement learning requires a large number of training interactions, which makes learning with real users time consuming and expensive. Many set-ups therefore rely on a user simulator instead of humans. These user simulators have their own problems. While hand-coded, rule-based user simulators have been shown to be sufficient in small, simple domains, for complex domains the number of rules quickly becomes intractable. State-of-the-art datadriven user simulators, on the other hand, are still domain-dependent. This means that adaptation to each new domain requires redesigning and retraining. In this work, we propose a domain-independent transformer-based user simulator (TUS). The structure of our TUS is not tied to a specific domain, enabling domain generalisation and learning of cross-domain user behaviour from data. We compare TUS with the state of the art using automatic as well as human evaluations. TUS can compete with rule-based user simulators on pre-defined domains and is able to generalise to unseen domains in a zero-shot fashion.

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A Generative Model for User Simulation in a Spatial Navigation Domain

Cited by 4 publications

References 23 publications

GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers

GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers

Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Domain-independent User Simulation with Transformers for Task-oriented Dialogue Systems

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