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Dialogue System Theory

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Book cover Statistical Methods for Spoken Dialogue Management

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Abstract

A spoken dialogue system has a large number of complex problems to overcome. To simplify matters, two key assumptions are almost always taken. First, only dialogues with exactly two participants are considered and second, all interactions between the system and the user are in the form of turns

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Notes

  1. 1.

    The name belief state is traditionally reserved in the literature for systems that use a particular statistical assumption, called “partial observability” (Sect. 2.3.2). However, even when this model is not used, the system’s internal state will always be a representation of its beliefs about what has happened in the dialogue. It is therefore reasonable to use the term for all models.

References

  • Austin JL (1962) How to do things with words. Oxford University Press, New York

    Google Scholar 

  • Black AW, Lenzo KA (2001) FLite: a small fast run-time synthesis engine. In: ISCA tutorial and research workshop on speech, synthesis

    Google Scholar 

  • Bohus D, Horvitz E (2009) Models for multiparty engagement in open-world dialog. In: Proceedings of SIGDIAL, pp 225–234

    Google Scholar 

  • Bohus D, Rudnicky A (2003) Ravenclaw: dialog management using hierarchical task decomposition and an expectation agenda. In: Proceedings of Eurospeech

    Google Scholar 

  • Bohus D, Rudnicky AI (2005) Constructing accurate beliefs in spoken dialog systems. In: Proceedings of ASRU, pp 272–277

    Google Scholar 

  • Bos J, Klein E, Lemon O, Oka T (2003) DIPPER: description and formalisation of an information-state update dialogue system architecture. In: Proceedings of SIGDIAL, pp 115–124

    Google Scholar 

  • Boyen X, Koller D (1998) Tractable inference for complex stochastic processes. In: Proceedings of uncertainty in AI. Morgan Kaufmann, San Francisco, pp 33–42

    Google Scholar 

  • Bui T, Poel M, Nijholt A, Zwiers J (2009) A tractable hybrid DDN-POMDP approach to affective dialogue modeling for probabilistic frame-based dialogue systems. Nat Lang Eng 15(2):273–307

    Article  Google Scholar 

  • Cassandra A, Littman ML, Zhang NL (1997) Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes. In: Proceedings of uncertainty in AI, pp 54–61

    Google Scholar 

  • Clark RA, Richmond K, King S (2004) Festival 2-build your own general purpose unit selection speech synthesiser. In: Proceedings of the ISCA workshop on speech, synthesis

    Google Scholar 

  • Cuayhuitl H, Renals S, Lemon, Shimodaira OH (2007) Hierarchical dialogue optimization using semi-Markov decision processes. In: Proceedings of interspeech

    Google Scholar 

  • Denecke M, Dohsaka K, Nakano M (2005) Fast reinforcement learning of dialogue policies using stable function approximation. In: Proceedings of IJCNLP. Springer, Heidelberg, pp 1–11

    Google Scholar 

  • Doshi F, Roy N (2007) Efficient model learning for dialog management. In: Proceedings of the ACM/IEEE international conference on human-robot interaction, pp 65–72. ACM. ISBN 978-1-59593-617-2

    Google Scholar 

  • Evermann, G Woodland PC (2000) Large vocabulary decoding and confidence estimation using word posterior probabilities. In: Proceedings of ICASSP

    Google Scholar 

  • Fodor P, Huerta JM (2006) Planning and logic programming for dialog management. In: Proceedings of SLT, pp 214–217

    Google Scholar 

  • Goddeau D, Meng H, Polifroni J, Seneff S, Busayapongchai S (1996) A form-based dialogue manager for spoken language applications. In: Proceedings of ICSLP

    Google Scholar 

  • He Y, Young S (2006) Spoken language understanding using the hidden vector state model. Speech Commun 48(3–4):262–275

    Article  Google Scholar 

  • Heeman PA (2007) Combining reinforcement learning with information-state update rules. In: Proceedings of HLT/NAACL, pp 268–275

    Google Scholar 

  • Henderson J, Lemon O (2008) Mixture model POMDPs for efficient handling of uncertainty in dialogue management. In: Proceedings of ACL/HLT, pp 73–76. Association for Computational Linguistics

    Google Scholar 

  • Henderson J, Lemon O, Georgila K (2005) Hybrid reinforcement/supervised learning for dialogue policies from communicator data. In: IJCAI workshop on knowledge and reasoning in practical dialogue systems, pp 68–75

    Google Scholar 

  • Horvitz E, Paek T (1999) A computational architecture for conversation. In: Proceedings of the seventh international conference on user modeling. Springer, Wien, pp 201–210

    Google Scholar 

  • Jiang H (2005) Confidence measures for speech recognition: a survey. Speech Commun 45(4):455–470

    Article  Google Scholar 

  • Jung S, Lee C, Kim K, Jeong M, Lee GG (2009) Data-driven user simulation for automated evaluation of spoken dialog systems. Comput Speech Lang 23(4):479–509

    Article  Google Scholar 

  • Jurcicek F, Gasic M, Keizer S, Mairesse F, Thomson B, Yu K, Young S (2009) Transformation-based learning for semantic parsing. In: Proceedings of SIGDIAL

    Google Scholar 

  • Kaelbling LP, Littman ML, Cassandra AR (1998) Planning and acting in partially observable stochastic domains. Artif Intell 101(1–2):99–134

    Article  MathSciNet  MATH  Google Scholar 

  • Kim D, Sim HS, Kim KE, Kim JH, Kim H, Sung JW (2008) Effects of user modeling on POMDP-based dialogue systems. In: Proceedings of interspeech

    Google Scholar 

  • Larsson S, Traum DR (2001) Information state and dialogue management in the TRINDI dialogue move engine toolkit. Nat Lang Eng 6(3 &4):323–340

    Google Scholar 

  • Lefevre F, Gasic M, Jurcicek F, Keizer S, Mairesse F, Thomson B, Yu K, Young S (2009) k-nearest neighbor Monte-Carlo control algorithm for POMDP-based dialogue systems. In: Proceedings of SIGDIAL

    Google Scholar 

  • Lemon O, Georgila K, Henderson J, Stuttle M (2006) An ISU dialogue system exhibiting reinforcement learning of dialogue policies: generic slot-filling in the TALK in-car system. In: Proceedings of EACL

    Google Scholar 

  • Levin E, Pieraccini R, Eckert W (2000) A stochastic model of human-machine interaction for learning dialog strategies. IEEE Trans Speech Audio Process 8(1):11–23

    Article  Google Scholar 

  • Levin E, Pieraccini R (1997) A stochastic model of computer-human interaction for learning dialogue strategies. In: Proceedings of EUROSPEECH, pp 1883–1886

    Google Scholar 

  • Litman DJ, Ros CP, Forbes-Riley K, VanLehn K, Bhembe D, Silliman S (2006) Spoken versus typed human and computer dialogue tutoring. Int J Artif Intell Educ 16(2):145–170

    Google Scholar 

  • Li L, Williams JD, Balakrishnan S (2009) Reinforcement learning for dialog management using least-squares policy iteration and fast feature selection. In: Proceedings of interspeech

    Google Scholar 

  • Mairesse F, Gasic M, Keizer FJ, Thomson B, Yu K, Young S (2009) Spoken language understanding from unaligned data using discriminative classification models. In: Proceedings of ICASSP

    Google Scholar 

  • Mairesse F, Walker M (2007) PERSONAGE: personality generation for dialogue. In: Proceedings of ACL

    Google Scholar 

  • McTear MF (1998) Modelling spoken dialogues with state transition diagrams: experiences with the CSLU toolkit. In: Proceedings of ICSLP

    Google Scholar 

  • Meng H, Wai C, Pieraccini R (2003) The use of belief networks for mixed-initiative dialog modeling. IEEE Trans Speech Audio Process 11(6):757–773

    Article  Google Scholar 

  • Paek T, Chickering D (2006) Evaluating the Markov assumption in Markov decision processes for spoken dialogue management. Lang Res Eval 40(1):47–66

    Article  Google Scholar 

  • Peters J, Schaal S (2008) Natural actor-critic. Neurocomputing 71:1180–1190

    Article  Google Scholar 

  • Peters J, Vijayakumar S, Schaal S (2005) Natural actor-critic. In: Proceedings of ECML. Springer, Heidelberg, pp 280–291

    Google Scholar 

  • Pieraccini R, Huerta JM (2008) Where do we go from here? In: Dybkjr L, Minker W (eds) Recent trends in discourse and dialogue. Text, speech and language technology, vol 39. Springer, Heidelberg

    Google Scholar 

  • Pietquin O, Renals S (2002) ASR system modeling for automatic evaluation and optimization of dialogue systems. In: Proceedings of ICASSP, pp 46–49

    Google Scholar 

  • Pulman S (1996) Conversational games, belief revision and Bayesian networks. In: Proceedings of the 7th computational linguistics in the Netherlands meeting

    Google Scholar 

  • Raux A, Bohus D, Langner B, Black AW, Eskenazi M (2006) Doing research on a deployed spoken dialogue system: one year of Let’s Go! experience. In: Proceedings of ICSLP

    Google Scholar 

  • Rich C, Sidner AL, Rich C, Sidner CL (1998) COLLAGEN: a collaboration manager for software interface agents. User Model User-Adapt Interact 8:315–350

    Google Scholar 

  • Roy N, Pineau J, Thrun S (2000) Spoken dialogue management using probabilistic reasoning. In: Proceedings of ACL

    Google Scholar 

  • Rudnicky A, Xu W (1999) An agenda-based dialog management architecture for spoken language systems. In: Proceedings of ASRU

    Google Scholar 

  • Schatzmann J, Weilhammer K, Stuttle M, Young S (2006) A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies. Knowl Eng Rev 21(02):97–126

    Article  Google Scholar 

  • Schatzmann J, Stuttle MN, Weilhammer K, Young S (2005) Effects of the user model on simulation-based learning of dialogue strategies. In: Proceedings of ASRU

    Google Scholar 

  • Schatzmann J, Thomson B, Young S (2007a) Error simulation for training statistical dialogue systems. In: Proceedings of ASRU, pp 526–531

    Google Scholar 

  • Schatzmann J, Thomson B, Young S (2007b) Statistical user simulation with a hidden agenda. In: Procedings of SIGDIAL, pp 273–282

    Google Scholar 

  • Scheffler K (2007) Automatic design of spoken dialogue systems. Ph.D. thesis, University of Cambridge

    Google Scholar 

  • Scheffler K, Young S (2001) Corpus-based dialogue simulation for automatic strategy learning and evaluation. In: Proceedings of NAACL

    Google Scholar 

  • Searle JR (1969) Speech acts: an essay in the philosophy of language. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Singh S, Litman D, Kearns M, Walker M (2002) Optimizing dialogue management with reinforcement learning: experiments with the NJFun system. J Artif Intell Res 16(1):105–133

    Google Scholar 

  • Sutton R, Barto A (1998) Reinforcement Learning: an introduction. Adaptive computation and machine learning. MIT Press, Cambridge

    Google Scholar 

  • Sutton S, Novick DG, Cole R, Vermeulen P, de Villiers J, Schalkwyk J, Fanty M (1996) Building 10,000 spoken dialogue systems. In: Proceedings of ICSLP

    Google Scholar 

  • Thomson B, Schatzmann J, Young S (2008) Bayesian update of dialogue state for robust dialogue systems. In: Proceedings of ICASSP, pp 4937–4940

    Google Scholar 

  • Traum DR (1999) Speech acts for dialogue agents. In: Wooldridge M, Rao A (eds) Foundation and theories of rational agents. Kluwer Academic, New York, pp 169–201

    Google Scholar 

  • Walker MA, Litman DJ, Kamm CA, Abella A (1997) PARADISE: a framework for evaluating spoken dialogue agents. In: Proceedings of ACL-EACL, pp 271–280

    Google Scholar 

  • Walker MA (2000) An application of reinforcement learning to dialogue strategy selection in a spoken dialogue system for email. J Artif Intell Res 12:387–416

    MATH  Google Scholar 

  • Walker W, Lamere P, Kwok P, Raj B, Singh R, Gouvea R, Wolf P, Woelfel J (2004) Sphinx-4: a flexible open source framework for speech recognition. Technical Report TR-2004-139, Sun Microsystems

    Google Scholar 

  • Williams JD (2007a) Applying POMDPs to dialog systems in the troubleshooting domain. In: Proceedings of the HLT/NAACL workshop on bridging the gap: academic and industrial research in dialog technology

    Google Scholar 

  • Williams JD (2007b) Using particle filters to track dialogue state. In: Proceedings of ASRU

    Google Scholar 

  • Williams JD (2008a) The best of both worlds: unifying conventional dialog systems and POMDPs. In: Proceedings of interspeech

    Google Scholar 

  • Williams JD (2008b) Integrating expert knowledge into POMDP optimization for spoken dialog systems. In: Proceedings of the AAAI workshop on advancements in POMDP solvers

    Google Scholar 

  • Williams JD, Young S (2005) Scaling up POMDPs for dialog management: the “Summary POMDP" method. In: Proceedings of ASRU, pp 177–182

    Google Scholar 

  • Williams JD, Young S (2006) Scaling POMDPs for dialog management with composite summary point-based value iteration (CSPBVI). In: Proceedings of the AAAI workshop on statistical and empirical approaches for spoken dialogue systems

    Google Scholar 

  • Williams JD, Young S (2007) Scaling POMDPs for spoken dialog management. IEEE Trans Audio Speech Lang Process 15:2116–2129

    Article  Google Scholar 

  • Wong YW, Mooney R (2007) Learning synchronous grammars for semantic parsing with lambda calculus. In: Proceedings of ACL, pp 960–967

    Google Scholar 

  • Young SJ, Williams JD, Schatzmann J, Stuttle MN, Weilhammer K (2005) The hidden information state approach to dialogue management. Technical Report CUED/FINFENG/TR.544, Cambridge University Engineering Department

    Google Scholar 

  • Young S, Kershaw D, Odell J, Ollason D, Valtchev V, Woodland P (2000) The HTK book version 3.0. Cambridge University Press, Cambridge

    Google Scholar 

  • Young S, Gasic M, Keizer S, Mairesse F, Schatzmann J, Thomson B, Yu K (2009) The hidden information state model: a practical framework for POMDP-based spoken dialogue management. Comput Speech Lang 24:150–174. ISSN 08852308

    Google Scholar 

  • Zen H, Nose T, Yamagishi J, Sako S, Masuko T, Black AW, Tokuda K (2007) The HMM-based speech synthesis system (HTS) version 2.0. In: Proceedings of ISCA, pp 294–299

    Google Scholar 

  • Zettlemoyer L, Collins M (2007) Online learning of relaxed CCG grammars for parsing to logical form. In: Proceedings of EMNLP, pp 678–687

    Google Scholar 

  • Zhang B, Cai Q, Mao J, Chang E, Guo B (2001) Spoken dialogue management as planning and acting under uncertainty. In: Seventh European conference on speech communication and technology

    Google Scholar 

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Correspondence to Blaise Thomson .

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Thomson, B. (2013). Dialogue System Theory. In: Statistical Methods for Spoken Dialogue Management. Springer Theses. Springer, London. https://doi.org/10.1007/978-1-4471-4923-1_2

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