Hostname: page-component-77c89778f8-m8s7h Total loading time: 0 Render date: 2024-07-17T07:08:10.888Z Has data issue: false hasContentIssue false
  • English
  • Français

Dialogue act recognition under uncertainty using Bayesian networks

Published online by Cambridge University Press:  01 December 2007

SIMON KEIZER  and
RIEKS OP DEN AKKER
SIMON KEIZER
Affiliation:
Computational Linguistics & Artificial Intelligence Group, Faculty of Arts, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands email: s.keizer@uvt.nl
RIEKS OP DEN AKKER
Affiliation:
Human Media Interaction Group, Department of Electrical Engineering, Mathematics and Computer Science, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands email: infrieks@cs.utwente.nl
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper we discuss the task of dialogue act recognition as a part of interpreting user utterances in context. To deal with the uncertainty that is inherent in natural language processing in general and dialogue act recognition in particular we use machine learning techniques to train classifiers from corpus data. These classifiers make use of both lexical features of the (Dutch) keyboard-typed utterances in the corpus used, and context features in the form of dialogue acts of previous utterances. In particular, we consider probabilistic models in the form of Bayesian networks to be proposed as a more general framework for dealing with uncertainty in the dialogue modelling process.

Type
Papers
Information
Natural Language Engineering , Volume 13 , Issue 4 , December 2007 , pp. 287 - 316
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alexandersson, J., Buschbeck-Wolf, B., Fujinami, T., Kipp, M., Koch, S., Maier, E., Reithinger, N., Schmitz, B. and Siegel, M. (1998) Dialogue acts in VERBMOBIL-2 second edition. VM-Report 226, DFKI GmbH.Google Scholar
Allen, J. and Core, M. (1997) Draft of DAMSL: Dialog Act Markup in Several Layers. Dagstuhl Workshop.Google Scholar
Andernach, T. (1996) A machine learning approach to the classification and prediction of dialogue utterances. Proceedings of the Second International Conference on New Methods in Language Processing (NeMLaP-2), pp. 98–109, Ankara, Turkey.Google Scholar
Andernach, T., Poel, M. and Salomons, E. (1997) Finding classes of dialogue utterances with Kohonen networks. In: Daelemans, W., van den Bosch, A. and Weijters, A., editors, Workshop Notes of the ECML/MLnet Workshop on Empirical Learning of Natural Language Processing Tasks, pp. 85–94, Prague, Czech Republic.Google Scholar
Austin, J. (1975) How to Do Things with Words. Harvard University Press, 2nd edition.CrossRefGoogle Scholar
Bilvi, M. and Pelachaud, C. (2003) Communicative and statistical eye gaze predictions. Workshop Embodied Conversational Characters as Individuals (in conjunction with AAMAS 2003.Google Scholar
Black, W., Thompson, P., Funk, A. and Conroy, A. (2003) Learning to classify utterances in a task-oriented dialogue. In: Jokinen, K., Gambäck, B., Black, W., Catizone, R. and Wilks, Y., editors, Proceedings of the Workshop on Dialogue Systems: Interaction, Adaptation and Styles of Management, pp. 9–16, Budapest.Google Scholar
Bunt, H. (1995). Dynamic interpretation and dialogue theory. In: Taylor, M., Bouwhuis, D. G. and Neel, F., editors, The Structure of Multimodal Dialogue, volume 2. John Benjamins, Amsterdam.Google Scholar
Bunt, H. (1996) Interaction management functions and context representation requirements. In: Luperfoy, S., Nijholt, A. and van Zanten, G. V., editors, Dialogue Management in Natural Language Systems, pp. 187–198. University of Twente.Google Scholar
Carletta, J., Isard, A., Isard, S., Kowtko, J., Doherty-Sneddon, G. and Anderson, A. (1996). HCRC Dialogue Structure Coding Manual. Technical Report TR-82, HCRC, Edinburgh.Google Scholar
Cooper, G. and Herskovits, E. (1992) A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309–347.Google Scholar
Galley, M., McKeown, K., Hirschberg, J. and Shriberg, E. (2004) Identifying agreement and disagreement in conversational speech: Use of Bayesian networks to model pragmatic dependencies. Proceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL'04), Main Volume, pp. 669–676, Barcelona, Spain.CrossRefGoogle Scholar
Goffman, E. (1981) Forms of Talk, chapter Replies and Responses, pages 5–77. University of Pennsylvania Press.Google Scholar
Heckerman, D. and Horvitz, E. (1998) Inferring informational goals from free-text queries: A Bayesian approach. Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 230–237, Madison, WI. Morgan Kaufmann.Google Scholar
Hofs, D., op den Akker, R., Nijholt, A. and Hondorp, H. (2003) A multimodal interaction system for navigation. In: Kruijff-Korbayova, I. and Kosny, C., editors, Proceedings Diabrück 2003, 7th Workshop on the Semantics and Pragmatics of Dialogue, pp. 183–184, Saarbrücken, Germany.Google Scholar
Jokinen, K., Hurtig, T., Hynnä, K., Kanto, K., Kaipainen, M. and Kerminen, A. (2001) Self-organizing dialogue management. Proceedings of the NLPRS Workshop ‘Neural Networks and Natural Language Processing’, pp. 78–85, Tokyo, Japan.Google Scholar
Keizer, S. (2001a) A Bayesian approach to dialogue act classification. In: Kühnlein, P., Rieser, H. and Zeevat, H., editors, BI-DIALOG 2001: Proceedings of the 5th Workshop on Formal Semantics and Pragmatics of Dialogue, pp. 210–218, Bielefeld, Germany.Google Scholar
Keizer, S. (2001b) Dialogue act modelling using Bayesian networks. In: Striegnitz, K., editor, Proceedings of the Sixth ESSLLI Student Session, pp. 143–153, Helsinki.Google Scholar
Keizer, S. (2003) Reasoning under Uncertainty in Natural Language Dialogue using Bayesian Networks. Number 2003–11 in SIKS Dissertation Series. Twente University Press, Enschede, The Netherlands.Google Scholar
Keizer, S., op den Akker, R. and Nijholt, A. (2002) Dialogue act recognition with Bayesian networks for Dutch dialogues. Proceedings 3rd ACL/SIGdial Workshop on Discourse and Dialogue, pp. 88–94, Philadelphia, PA.CrossRefGoogle Scholar
Kipp, M. (1998) The neural path to dialogue acts. In: Prade, H., editor, Proceedings of the 13th European Conference on Artificial Intelligence, pp. 175–179.Google Scholar
Luin, J., van op den Akker, R. and Nijholt, A. (2001) A dialogue agent for navigation support in virtual reality. In: Jacko, J. and Sears, A., editors, ACM SIGCHI Conf. CHI 2001: Anyone. Anywhere, pp. 117–118, Seattle. Association for Computing Machinery.Google Scholar
Nagata, M. and Morimoto, T. (1994) First steps towards statistical modeling of dialogue to predict the speech act type of the next utterance. Speech Communication, 15:193–203.Google Scholar
Nijholt, A. (2000) Multimodal interactions with agents in virtual worlds. In: Kasabov, N. editor, Future Directions for Intelligent Systems and Information Science, Physica-Verlag: Studies in Fuzziness and Soft Computing, chapter 8, pp. 148–173. Springer.Google Scholar
Nijholt, A., Zwiers, J. and van Dijk, B. (2001) Maps, agents and dialogue for exploring a virtual world. In: Callaos, N., Long, S. and Loutfi, M., editors, 5th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2001), volume VII of Human Information and Education Systems, pp. 94–99, Orlando.Google Scholar
Pearl, J. (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.Google Scholar
Poesio, M. and Traum, D. (1997) Conversational actions and discourse situations. Computational Intelligence, 13 (3):309–347.Google Scholar
Poesio, M. and Traum, D. (1998) Towards an Axiomatization of Dialogue Acts. In: Hulstijn, J. and Nijholt, A., editors, TwenDial'98: Formal Semantics and Pragmatics of Dialogue, no. 13 in TWLT.Google Scholar
Pulman, S. (1996) Conversational games, belief revision and Bayesian networks. In: Landsbergen, J., Odijk, J., van Deemter, K. and van Zanten, G. V., editors, Computational Linguistics in the Netherlands. SRI Technical Report CRC-071.Google Scholar
Reithinger, N. and Klesen, M. (1997) Dialogue act classification using language models. In: Kokkinakis, G., Fakotakis, N. and Dermatas, E., editors, Proceedings of the 5th European Conference on Speech Communication and Technology EuroSpeech-97, volume 4, pp. 2235–2238, Rhodes.Google Scholar
Samuel, K., Carberry, S., and Vijay-Shanker, K. (1998). Dialogue act tagging with transformation-based learning. Proceedings of the 36th Annual Meeting of the ACL and the 17th International Conference on Computational Linguistics (ACL-COLING), pp. 1150–1156.Google Scholar
Searle, J. (1969) Speech Acts: An Essay in the Philosophy of Language. Cambridge University Press.Google Scholar
Schegloff, E. and Sacks, H. (1973) Opening up closings. Semiotica, 8:289–327.Google Scholar
Shriberg, E., Dhillon, R., Bhagat, S., Ang, J. and Carvey, H. (2004) The ICSI meeting recorder dialog act (MRDA) corpus. In: Strube, M. and Sidner, C., editors, Proceedings of the 5th SIGdial Workshop on Discourse and Dialogue, pp. 97–100, Cambridge, Massachusetts, April 30–May 1 2004. Association for Computational Linguistics.Google Scholar
Stolcke, A., Coccaro, N., Bates, R., Taylor, P., Ess-Dykema, C. V., Ries, K., Shriberg, E., Jurafsky, D., Martin, R. and Meteer, M. (2000) Dialogue act modelling for automatic tagging and recognition of conversational speech. Computational Linguistics, 26 (3): 339–374.Google Scholar
Traum, D. (2000) 20 questions on dialogue act taxonomies. Journal of Semantics, 17 (1): 7–30.CrossRefGoogle Scholar
Witten, I. and Frank, E. (1999). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann. URL: http://www.cs.waikato.ac.nz/~ml/weka/.Google Scholar
Wittgenstein, L. (1958) Philosophical Investigations. Blackwell Publishers, 2nd edition.Google Scholar
Wright, H. (1998) Automatic utterance type detection using suprasegmental features. Int. Conf. on Spoken Language Processing, volume 4, pp. 1403–1406, Sydney.Google Scholar
Wright, H., Poesio, M. and Isard, S. (1999) Using high level dialogue information for dialogue act recognition using prosodic features. ESCA Tutorial and Research Workshop on Dialogue and Prosody, pp. 139–143.Google Scholar