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Pattern Analysis and Applications

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30.07.2018 | Original Article

User-aware dialogue management policies over attributed bi-automata

verfasst von: Manex Serras, María Inés Torres, Arantza del Pozo

Erschienen in: Pattern Analysis and Applications | Ausgabe 4/2019

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Abstract

Designing dialogue policies that take user behavior into account is complicated due to user variability and behavioral uncertainty. Attributed probabilistic finite-state bi-automata (A-PFSBA) have proven to be a promising framework to develop dialogue managers that capture the users’ actions in its structure and adapt to them online, yet developing policies robust to high user uncertainty is still challenging. In this paper, the theoretical A-PFSBA dialogue management framework is augmented by formally defining the notation of exploitation policies over its structure. Under such definition, multiple path-based policies are implemented, those that take into account external information and those which do not. These policies are evaluated on the Let’s Go corpus, before and after an online learning process whose goal is to update the initial model through the interaction with end users. In these experiments the impact of user uncertainty and the model structural learning is thoroughly analyzed.

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In order to avoid numerical underflow, the logarithm is applied to the product.

\(\eta\) = 0.25.

95% confidence interval.

In social dialogue systems the longer the dialogue the better, as their goal is to maximize the user engagement with the system.

Bohus D, Rudnicky AI (2009) The ravenclaw dialog management framework: architecture and systems. Comput Speech Lang 23(3):332–361CrossRef

Cuayáhuitl H (2017) Simpleds: a simple deep reinforcement learning dialogue system. In: Dialogues with social robots, Springer, Berlin, pp 109–118

Gašić M, Jurčíček F, Keizer S, Mairesse F, Thomson B, Yu K, Young S (2010) Gaussian processes for fast policy optimisation of pomdp-based dialogue managers. In: Proceedings of the 11th annual meeting of the special interest group on discourse and dialogue, association for computational linguistics, pp 201–204

Ghigi F, Torres MI (2015) Decision making strategies for finite-state bi-automaton in dialog management. In: Natural language dialog systems and intelligent assistants, Springer, Berlin, pp 209–221

Gorin AL, Riccardi G, Wright JH (1997) How may i help you? Speech Commun 23(1–2):113–127CrossRefMATH

Griol D, Callejas Z, López-Cózar R (2010) Statistical dialog management methodologies for real applications. In: Proceedings of the 11th annual meeting of the special interest group on discourse and dialogue, association for computational linguistics, pp 269–272

Hurtado LF, Griol D, Sanchis E, Segarra E (2007) A statistical user simulation technique for the improvement of a spoken dialog system. In: Iberoamerican congress on pattern recognition, Springer, Berlin, pp 743–752

Hurtado LF, Planells J, Segarra E, Sanchis E, Griol D (2010) A stochastic finite-state transducer approach to spoken dialog management. In: Eleventh annual conference of the international speech communication association

Jurčíček F, Thomson B, Young S (2012) Reinforcement learning for parameter estimation in statistical spoken dialogue systems. Comput Speech Lang 26(3):168–192CrossRef

10.

Li J, Monroe W, Ritter A, Galley M, Gao J, Jurafsky D (2016) Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:160601541

11.

Li Z, Tang J (2017) Weakly supervised deep matrix factorization for social image understanding. IEEE Trans Image Process 26(1):276–288MathSciNetCrossRefMATH

12.

Li Z, Liu J, Tang J, Lu H (2015) Robust structured subspace learning for data representation. IEEE Trans Pattern Anal Mach Intell 37(10):2085–2098CrossRef

13.

Orozko OR, Torres MI (2015) Online learning of stocastic bi-automaton to model dialogues. In: Iberian conference on pattern recognition and image analysis, Springer, Berlin, pp 441–451

14.

Raux A, Langner B, Bohus D, Black AW, Eskenazi M (2005) Let’s go public! Taking a spoken dialog system to the real world. In: Ninth European conference on speech communication and technology

15.

Schatzmann J, Young S (2009) The hidden agenda user simulation model. IEEE Trans Audio Speech Lang Process 17(4):733–747CrossRef

16.

Schatzmann J, Georgila K, Young S (2005) Quantitative evaluation of user simulation techniques for spoken dialogue systems. In: 6th SIGDIAL workshop on discourse and dialogue

17.

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(2):97–126CrossRef

18.

Serban IV, Sordoni A, Bengio Y, Courville AC, Pineau J (2016) Building end-to-end dialogue systems using generative hierarchical neural network models. AAAI 16:3776–3784

19.

Serras M, Torres MI, Del Pozo A (2017a) Online learning of attributed bi-automata for dialogue management in spoken dialogue systems. In: Iberian conference on pattern recognition and image analysis, Springer, Berlin, pp 22–31

20.

Serras M, Torres MI, del Pozo A (2017b) Regularized neural user model for goal oriented spoken dialogue systems. In: International workshop on spoken dialogue systems (IWSDS)

21.

Sutton RS, Barto AG (1998) Reinforcement learning: an introduction, vol 1. MIT Press, CambridgeMATH

22.

Thomson B, Yu K, Keizer S, Gašić M, Jurčíček F, Mairesse F, Young S (2010) Bayesian dialogue system for the let’s go spoken dialogue challenge. In: Spoken language technology workshop (SLT), 2010 IEEE, IEEE, pp 460–465

23.

Torres MI (2013) Stochastic bi-languages to model dialogs. In: Proceedings of the 11th international conference on finite state methods and natural language processing, pp 9–17

24.

Torres MI, Benedí JM, Justo R, Ghigi F (2012) Modeling spoken dialog systems under the interactive pattern recognition framework. In: Joint IAPR international workshops on statistical techniques in pattern recognition (SPR) and structural and syntactic pattern recognition (SSPR), Springer, Berlin, pp 519–528

25.

Vinyals O, Le Q (2015) A neural conversational model. arXiv preprint arXiv:150605869

26.

Ward W (1995) The CMU atis system. In: Proceedings of ARPA spoken language systems technology workshop

27.

Williams JD, Zweig G (2016) End-to-end lstm-based dialog control optimized with supervised and reinforcement learning. arXiv preprint arXiv:160601269

28.

Young S, Gašić M, Thomson B, Williams JD (2013) Pomdp-based statistical spoken dialog systems: a review. Proc IEEE 101(5):1160–1179CrossRef

29.

Zhao T, Eskenazi M (2016) Towards end-to-end learning for dialog state tracking and management using deep reinforcement learning. In: 17th annual meeting of the special interest group on discourse and dialogue, p 1

Titel: User-aware dialogue management policies over attributed bi-automata
verfasst von: Manex Serras
María Inés Torres
Arantza del Pozo
Publikationsdatum: 30.07.2018
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 4/2019
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-018-0743-y

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