A data-driven approach to spoken dialog segmentation

Abstract In this paper, we present a statistical model for spoken dialog segmentation that decides the current phase of the dialog by means of an automatic classification process. We have applied our proposal to three practical conversational systems acting in different domains. The results of the evaluation show that is possible to attain high accuracy rates in dialog segmentation when using different sources of information to represent the user input. Our results indicate how the module proposed can also improve dialog management by selecting better system answers. The statistical model developed with human-machine dialog corpora has been applied in one of our experiments to human-human conversations and provides a good baseline as well as insights in the model limitation.

[1]  Milica Gasic,et al.  POMDP-Based Statistical Spoken Dialog Systems: A Review , 2013, Proceedings of the IEEE.

[2]  Steve J. Young,et al.  Partially observable Markov decision processes for spoken dialog systems , 2007, Comput. Speech Lang..

[3]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[4]  David R. Traum,et al.  CONVERSATION ACTS IN TASK‐ORIENTED SPOKEN DIALOGUE , 1992, Comput. Intell..

[5]  David Griol,et al.  A statistical simulation technique to develop and evaluate conversational agents , 2013, AI Commun..

[6]  Noriaki Horii,et al.  A multichannel convolutional neural network for cross-language dialog state tracking , 2016, 2016 IEEE Spoken Language Technology Workshop (SLT).

[7]  Uwe Reyle,et al.  From Discourse to Logic - Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and Discourse Representation Theory , 1993, Studies in linguistics and philosophy.

[8]  Evgeny A. Stepanov,et al.  The Development of the Multilingual LUNA Corpus for Spoken Language System Porting , 2014, LREC.

[9]  Encarna Segarra,et al.  Error handling in a stochastic dialog system through confidence measures , 2005, Speech Commun..

[10]  Elizabeth Shriberg,et al.  Automatic dialog act segmentation and classification in multiparty meetings , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[11]  W. Mann,et al.  Rhetorical Structure Theory: looking back and moving ahead , 2006 .

[12]  Matthew Henderson,et al.  Machine Learning for Dialog State Tracking: A Review , 2015 .

[13]  Antoine Raux,et al.  The Dialog State Tracking Challenge Series: A Review , 2016, Dialogue Discourse.

[14]  Jun-Wei Mao,et al.  Speech emotion recognition based on feature selection and extreme learning machine decision tree , 2018, Neurocomputing.

[15]  Plamen P. Angelov,et al.  Evolving Fuzzy-Rule-Based Classifiers From Data Streams , 2008, IEEE Transactions on Fuzzy Systems.

[16]  Milica Gasic,et al.  Bayesian dialogue system for the Let's Go Spoken Dialogue Challenge , 2010, 2010 IEEE Spoken Language Technology Workshop.

[17]  W. Bruce Croft,et al.  Text Segmentation by Topic , 1997, ECDL.

[18]  Fei Liu,et al.  Dialog state tracking, a machine reading approach using Memory Network , 2016, EACL.

[19]  Staffan Larsson,et al.  Information state and dialogue management in the TRINDI dialogue move engine toolkit , 2000, Natural Language Engineering.

[20]  Gary Geunbae Lee,et al.  Natural Language Dialog Systems and Intelligent Assistants , 2015, Springer International Publishing.

[21]  Justin Zobel,et al.  Passage retrieval revisited , 1997, SIGIR '97.

[22]  Jason D. Williams,et al.  Web-style ranking and SLU combination for dialog state tracking , 2014, SIGDIAL Conference.

[23]  Marie-Francine Moens,et al.  The use of topic segmentation for automatic summarization , 2002, ACL 2002.

[24]  L. Polanyi The Linguistic Structure of Discourse , 2005 .

[25]  Gökhan Tür,et al.  Cascaded model adaptation for dialog act segmentation and tagging , 2010, Comput. Speech Lang..

[26]  David Griol,et al.  A framework for improving error detection and correction in spoken dialog systems , 2016, Soft Comput..

[27]  Alistair Moffat,et al.  Efficient Retrieval of Partial Documents , 1995, Inf. Process. Manag..

[28]  Jason D. Williams Challenges and Opportunities for State Tracking in Statistical Spoken Dialog Systems: Results From Two Public Deployments , 2012, IEEE Journal of Selected Topics in Signal Processing.

[29]  Salvador España Boquera,et al.  Efficient BP Algorithms for General Feedforward Neural Networks , 2007, IWINAC.

[30]  Rebecca J. Passonneau,et al.  Discourse Segmentation by Human and Automated Means , 1997, CL.

[31]  Candace L. Sidner,et al.  Attention, Intentions, and the Structure of Discourse , 1986, CL.

[32]  Elizabeth Shriberg,et al.  Meeting Recorder Project: Dialog Act Labeling Guide , 2004 .

[33]  David Griol,et al.  The Conversational Interface: Talking to Smart Devices , 2016 .

[34]  Iñigo Casanueva,et al.  Deep Learning for Conversational AI , 2018, NAACL.

[35]  David Griol,et al.  FRB-Dialog: A Toolkit for Automatic Learning of Fuzzy-Rule Based (FRB) Dialog Managers , 2017, HAIS.

[36]  Nicholas Asher,et al.  Reference to abstract objects in discourse , 1993, Studies in linguistics and philosophy.

[37]  Jan Alexandersson Plan recognition in verbmobil , 1995 .

[38]  Andy P. Field,et al.  Discovering Statistics Using Ibm Spss Statistics , 2017 .

[39]  John D. Lafferty,et al.  Statistical Models for Text Segmentation , 1999, Machine Learning.

[40]  Martha E. Pollack,et al.  The Uses of Plans , 1992, Artif. Intell..

[41]  Helen F. Hastie,et al.  “Let's Go, DUDE!” using the Spoken Dialogue Challenge to teach Spoken Dialogue development , 2010, 2010 IEEE Spoken Language Technology Workshop.

[42]  Tomoki Toda,et al.  Learning cooperative persuasive dialogue policies using framing , 2016, Speech Commun..

[43]  Yonghong Yan,et al.  Markovian Discriminative Modeling for Dialog State Tracking , 2014, SIGDIAL Conference.

[44]  William C. Mann,et al.  Rhetorical Structure Theory: Toward a functional theory of text organization , 1988 .

[45]  Angeliki Metallinou,et al.  Discriminative state tracking for spoken dialog systems , 2013, ACL.

[46]  Steve J. Young,et al.  Error simulation for training statistical dialogue systems , 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).

[47]  Joyce Chai,et al.  Discourse Structure for Context Question Answering , 2004, HLT-NAACL 2004.

[48]  David Griol,et al.  A statistical approach to spoken dialog systems design and evaluation , 2008, Speech Commun..

[49]  Igor Chikalov,et al.  Bi-criteria optimization of decision trees with applications to data analysis , 2018, Eur. J. Oper. Res..

[50]  Maxine Eskénazi,et al.  Let's go public! taking a spoken dialog system to the real world , 2005, INTERSPEECH.