Top

Published in:

2021 | OriginalPaper | Chapter

Managing Multi-task Dialogs by Means of a Statistical Dialog Management Technique

Authors : David Griol, Zoraida Callejas, Jose F. Quesada

Published in: Increasing Naturalness and Flexibility in Spoken Dialogue Interaction

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

One of the most demanding tasks when developing a dialog system consists of deciding the next system response considering the user’s actions and the dialog history, which is the fundamental responsibility related to dialog management. A statistical dialog management technique is proposed in this work to reduce the effort and time required to design the dialog manager. This technique allows not only an easy adaptation to new domains, but also to deal with the different subtasks for which the dialog system has been designed. The practical application of the proposed technique to develop a dialog system for a travel-planning domain shows that the use of task-specific dialog models increases the quality and number of successful interactions with the system in comparison with developing a single dialog model for the complete domain.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Transfer Learning for Unseen Slots in End-to-End Dialogue State Tracking

next chapter Generating Supportive Utterances for Open-Domain Argumentative Dialogue Systems

Bangalore S, DiFabbrizio G, Stent A (2008) Learning the structure of task-driven human-human dialogs. IEEE Trans Audio Speech Lang Process 16(7):1249–1259CrossRef

Chotimongkol A (2008) Learning the structure of task-oriented conversations from the corpus of in-domain dialogs. Ph.D. thesis, CMU, Pittsburgh (USA)

Cuayáhuitl H, Renals S, Lemon O, Shimodaira H (2005) Human-computer dialogue simulation using hidden Markov models. In: Proceedings of the IEEE workshop on automatic speech recognition and understanding (ASRU’05). San Juan, Puerto Rico, pp 290–295

Frampton M, Lemon O (2009) Recent research advances in reinforcement learning in spoken dialogue systems. Knowl Eng Rev 24(4):375–408CrossRef

Griol D, Callejas Z, López-Cózar R, Riccardi G (2014) A domain-independent statistical methodology for dialog management in spoken dialog systems. Comput Speech Lang 28(3):743–768CrossRef

Griol D, Carbó J, Molina J (2013) A statistical simulation technique to develop and evaluate conversational agents. AI Commun 26(4):355–371MathSciNetCrossRef

Griol D, Iglesias J, Ledezma A, Sanchis A (2016) A two-stage combining classifier model for the development of adaptive dialog systems. Int J Neural Syst 26(1)

Lee C, Jung S, Kim K, Lee GG (2010) Hybrid approach to robust dialog management using agenda and dialog examples. Comput Speech Lang 24(4):609–631CrossRef

Levin E, Pieraccini R (1997) A stochastic model of human-machine interaction for learning dialog strategies. In: Proceedings of the European conference on speech communications and technology (Eurospeech’97). Rhodes, Greece, pp 1883–1896

10.

Lison P (2015) A hybrid approach to dialogue management based on probabilistic rules. Comput Speech Lang 34(1):232–255CrossRef

11.

McTear M, Callejas Z, Griol D (2016) The conversational interface. Springer, Berlin

12.

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

13.

Metallinou A, Bohus D, Williams J (2013) Discriminative state tracking for spoken dialog systems. In: Proceedings of ACL’13. Sofia, Bulgaria, pp 466–475

14.

Pérez J, Liu F (2017) Dialog state tracking, a machine reading approach using Memory Network. In: Proceedings of the 15th conference of the European chapter of the association for computational linguistics (EACL). Valencia, Spain, pp 305–314

15.

Planells J, Hurtado L, Sanchis E, Segarra E (2012) An online generated transducer to increase dialog manager coverage. In: Proceedings of the international conference on spoken language processing (Interspeech’2012). Portland, USA

16.

Roy N, Pineau J, Thrun S (2000) Spoken dialogue management using probabilistic reasoning. In: Proceedings of the 38th annual meeting of the association for computational linguistics (ACL’00). Hong Kong, China, pp 93–100

17.

Schatzmann J, Thomson B, Young S (2007) Error simulation for training statistical dialogue systems. In: Proceedings of IEEE automatic speech recognition and understanding workshop (ASRU’07). Kyoto, Japan, pp 273–282

18.

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

19.

Su P, Mrksic N, Casanueva I, Vulic I (2018) Deep learning for conversational AI. In: Proceeings of the 2018 conference of the North American chapter of the association for computational linguistics (NAACL-HLT 2018). New Orleans, Louisiana, USA, pp 27–32

20.

Williams J, Raux A, Henderson M (2016) The dialog state tracking challenge series: a review. Dialogue Discourse 7(3):4–33CrossRef

21.

Williams J, Young S (2007) Partially observable Markov decision processes for spoken dialog systems. Comput Speech Lang 21(2):393–422CrossRef

22.

Young S (2013) Talking to machines. R Acad Eng Ingenia 54:40–46

23.

Young S, Schatzmann J, Weilhammer K, Ye H (2007) The hidden information state approach to dialogue management. In: Proceedings of the 32nd IEEE international conference on acoustics, speech, and signal processing (ICASSP). Honolulu, Haway, USA, pp 149–152

24.

Zhou Y, Hu Q, Liu J, Jia Y (2015) Combining heterogeneous deep neural networks with conditional random fields for chinese dialogue act recognition. Neurocomputing 168:408–417CrossRef

Title: Managing Multi-task Dialogs by Means of a Statistical Dialog Management Technique
Authors: David Griol
Zoraida Callejas
Jose F. Quesada
Publisher: Springer Singapore
Book: Increasing Naturalness and Flexibility in Spoken Dialogue Interaction
Print ISBN: 978-981-15-9322-2

Electronic ISBN: 978-981-15-9323-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-981-15-9323-9_6