Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-76c49bb84f-c7tcl Total loading time: 0 Render date: 2025-07-05T09:35:53.035Z Has data issue: false hasContentIssue false

3 - Pursuing and demonstrating understanding in dialogue

from Part I - Joint construction

Published online by Cambridge University Press:  05 July 2014

By
David DeVault  and
Matthew Stone
Edited by
Amanda Stent  and
Srinivas Bangalore
David DeVault
Affiliation:
University of Southern
Matthew Stone
Affiliation:
State University of New Jersey
Amanda Stent
Affiliation:
AT&T Research, Florham Park, New Jersey
Srinivas Bangalore
Affiliation:
AT&T Research, Florham Park, New Jersey
Get access

Summary

Introduction

The appeal of natural language dialogue as an interface modality is its ability to support open-ended mixed-initiative interaction. Many systems offer rich and extensive capabilities, but must support novice or infrequent users. It is unreasonable to expect untrained users to know the actions they need in advance, or to be able to specify their goals using a regimented scheme of commands or menu options. Dialogue allows the user to talk through their needs with the system and arrive collaboratively at a feasible solution. Dialogue, in short, becomes more useful to users as the interaction becomes more potentially problematic.

However, the flexibility of dialogue comes at a cost in system engineering. We cannot expect the user's model of the task and domain to align with the system's. Consequently, the system cannot count on a fixed schema to enable it to understand the user. It must be prepared for incorrect or incomplete analyses of users' utterances, and must be able to put together users' needs across extended interactions. Conversely, the system must be prepared for users that misunderstand it, or fail to understand it.

This chapter provides an overview of the concepts, models, and research challenges involved in this process of pursuing and demonstrating understanding in dialogue. We start in Section 3.2 from analyses of human–human conversation. People are no different from systems: they, too, face potentially problematic interactions involving misunderstandings. In response, they avail themselves of a wide range of discourse moves and interactive strategies, suggesting that they approach communication itself as a collaborative process wherein all parties establish agreement, to their mutual satisfaction, on the distinctions that matter for their discussion and on the expressions through which to identify those distinctions. In the literature, this process is often described as grounding communication, or identifying contributions well enough so that they become part of the common ground of the conversation (Clark and Marshall, 1981; Clark and Schaefer, 1989; Clark and Wilkes-Gibbs, 1990; Clark, 1996).

Information

Type
Chapter
Information
Natural Language Generation in Interactive Systems , pp. 34 - 62
Publisher: Cambridge University Press
Print publication year: 2014

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.)

Book purchase

Temporarily unavailable

References

Allen, J. F., Blaylock, N., and Ferguson, G. (2002). A problem solving model for collaborative agents. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 774-781, Bologna, Italy. International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
Asher, N. and Lascarides, A. (2003). Logics of Conversation. Cambridge University Press, Cambridge, UK.Google Scholar
Brennan, S. E. (1990). Seeking and Providing Evidence for Mutual Understanding. PhD thesis, Department of Psychology, Stanford University.Google Scholar
Brennan, S. E. and Clark, H. H. (1996). Conceptual pacts and lexical choice in conversation. Journal of Experimental Psychology, 22(6):1482-1493.Google ScholarPubMed
Brennan, S. E. and Williams, M. (1995). The feeling of another's knowing: Prosody and filled pauses as cues to listeners about the metacognitive states of speakers. Journal of Memory and Language, 34(3):383-398.CrossRefGoogle Scholar
Bunt, H. (1994). Context and dialogue control. THINK Quarterly, 3:19-31.Google Scholar
Bunt, H. (1996). Interaction management functions and context representation requirements. In Proceedings ofthe Twente Workshop on Language Technology, pages 187-198, University of Twente. University of Twente.Google Scholar
Bunt, H. (2000). Dialogue pragmatics and context specification. In Bunt, H. and Black, W., editors, Abduction, Beliefand Context in Dialogue. Studies in Computational Pragmatics, pages 81-150. John Benjamins, Amsterdam, The Netherlands.CrossRefGoogle Scholar
Carberry, S. and Lambert, L. (1999). A process model for recognizing communicative acts and modeling negotiation subdialogues. Computational Linguistics, 25(1):1-53.Google Scholar
Cassell, J. (2000). Embodied conversational interface agents. Communications of the ACM, 43(4):70-78.CrossRefGoogle Scholar
Cassell, J., Stone, M., and Yan, H. (2000). Coordination and context-dependence in the generation of embodied conversation. In Proceedings ofthe International Conference on Natural Language Generation (INLG), pages 171-178, Mitzpe Ramon, Israel. Association for Computational Linguistics.Google Scholar
Clark, H. (1996). Using Language. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
Clark, H. H. (1993). Arenas ofLanguage Use. University of Chicago Press, Chicago, IL.Google Scholar
Clark, H. H. and Krych, M. (2004). Speaking while monitoring addressees for understanding. Journal of Memory and Language, 50(1):62-81.CrossRefGoogle Scholar
Clark, H. H. and Marshall, C. R. (1981). Definite reference and mutual knowledge. In Joshi, A., Webber, B., and Sag, I., editors, Elements of Discourse Understanding, pages 10-63. Cambridge University Press, Cambridge, UK.Google Scholar
Clark, H. H. and Schaefer, E. F. (1989). Contributing to discourse. Cognitive Science, 13(2): 259-294.CrossRefGoogle Scholar
Clark, H. H. and Wilkes-Gibbs, D. (1990). Referring as a collaborative process. In Cohen, P. R., Morgan, J., and Pollack, M. E., editors, Intentions in Communication, pages 463-493. MIT Press, Cambridge, MA.Google Scholar
Cohen, P. R. (1997). Dialogue modeling. In Cole, R., Mariani, J., Uszkoreit, H., Varile, G. B., Zaenen, A., and Zampolli, A., editors, Survey of the State of the Art in Human Language Technology (Studies in Natural Language Processing), pages 204-210. Cambridge University Press, Cambridge, UK.Google Scholar
Core, M. G. and Allen, J. F. (1997). Coding dialogues with the DAMSL annotation scheme. In Working Notes of the AAAI Fall Symposium on Communicative Action in Humans and Machines, Boston, MA. AAAI Press.Google Scholar
DeVault, D. (2008). Contribution Tracking: Participating in Task-Oriented Dialogue under Uncertainty. PhD thesis, Department of Computer Science, Rutgers, The State University of New Jersey, New Brunswick, NJ.Google Scholar
DeVault, D., Kariaeva, N., Kothari, A., Oved, I., and Stone, M. (2005). An information-state approach to collaborative reference. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pages 1-4, Ann Arbor, MI. Association for Computational Linguistics.Google Scholar
DeVault, D. and Stone, M. (2006). Scorekeeping in an uncertain language game. In Proceedings of the Workshop on the Semantics and Pragmatics of Dialogue (brandial), pages 139-146, Potsdam, Germany. SemDial.Google Scholar
DeVault, D. and Stone, M. (2007). Managing ambiguities across utterances in dialogue. In Proceedings ofthe Workshop on the Semantics and Pragmatics of Dialogue (DECALOG), pages 49-56, Rovereto, Italy. SemDial.Google Scholar
DeVault, D. and Stone, M. (2009). Learning to interpret utterances using dialogue history. In Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL), pages 184-192, Athens, Greece. Association for Computational Linguistics.Google Scholar
Di Eugenio, B., Jordan, P. W., Thomason, R. H., and Moore, J. D. (2000). The agreement process: An empirical investigation of human-human computer-mediated collaborative dialogue. International Journal of Human-Computer Studies, 53(6):1017-1076.CrossRefGoogle Scholar
Furnas, G. W., Landauer, T. K., Gomez, L. M., and Dumais, S. T. (1987). The vocabulary problem in human-system communications. Communications of the ACM, 30(11):964-971.CrossRefGoogle Scholar
Ginzburg, J. and Cooper, R. (2004). Clarification, ellipsis and the nature of contextual updates in dialogue. Linguistics and Philosophy, 27(3):297-365.CrossRefGoogle Scholar
Goldman, A. (1970). A Theory ofHuman Action. Prentice Hall, Upper Saddle River, NJ.Google Scholar
Gregoromichelaki, E., Kempson, R., Purver, M., Mills, G. J., Cann, R., Meyer-Viol, W., and Healey, P. G. (2011). Incrementality and intention-recognition in utterance processing. Dialogue and Discourse, 2(1):199-233.CrossRefGoogle Scholar
Grice, H. P. (1975). Logic and conversations. In Cole, P. and Morgan, J. L., editors, Syntax and Semantics III: Speech Acts, pages 41-58. Academic Press, New York, NY.Google Scholar
Grosz, B. J. and Sidner, C. L. (1986). Attention, intentions, and the structure of discourse. Computational Linguistics, 12(3):175-204.Google Scholar
Heeman, P. A. and Hirst, G. (1995). Collaborating on referring expressions. Computational Linguistics, 21(3):351-383.Google Scholar
Henderson, J., Lemon, O., and Georgila, K. (2008). Hybrid reinforcement/supervised learning of dialogue policies from fixed datasets. Computational Linguistics, 34(4):487-513.CrossRefGoogle Scholar
Hobbs, J. R., Stickel, M., Appelt, D., and Martin, P. (1993). Interpretation as abduction. Artificial Intelligence, 63(1-2):69-142.CrossRefGoogle Scholar
Horvitz, E. and Paek, T. (2001). Harnessing models of users' goals to mediate clarification dialog in spoken language systems. In Proceedings of the International Conference on User Modeling, pages 3-13, Sonthofen, Germany. Springer.Google Scholar
Kehler, A. (2001). Coherence, Reference and the Theory of Grammar. CSLI Publications, Stanford, CA.Google Scholar
Kopp, S., Tepper, P., and Cassell, J. (2004). Towards integrated rnicroplanning of language and iconic gesture for multimodal output. In Proceedings of the International Conference on Multimodal Interfaces (ICMI), pages 97-104, State College, PA. Association for Computing Machinery.Google Scholar
Larsson, S. and Traum, D. (2000). Information state and dialogue management in the TRINDI dialogue move engine toolkit. Natural Language Engineering, 6(3-1):323-340.CrossRefGoogle Scholar
Lascarides, A. and Asher, N. (2009). Agreement, disputes and commitments in dialogue. Journal of Semantics, 26(2):109-158.CrossRefGoogle Scholar
Lascarides, A. and Stone, M. (2009). Discourse coherence and gesture interpretation. Gesture, 9(2):147-180.CrossRefGoogle Scholar
Lemon, O. (2011). Learning what to say and how to say it: Joint optimisation of spoken dialogue management and natural language generation. Computer Speech & Language, 25(2):210-221.CrossRefGoogle Scholar
Levin, E. and Pieraccini, R. (1997). A stochastic model of computer-human interaction for learning dialogue strategies. In Proceedings ofthe European Conference on Speech Communication and Technology (EUROSPEECH), pages 1883-1886, Rhodes, Greece. International Speech Communication Association.Google Scholar
Levin, E., Pieraccini, R., and Eckert, W. (1998). Using Markov decision process for learning dialogue strategies. In Proceedings of the IEEE International Conference on Acoustics, Speech, andSignal Processing (ICASSP), volume 1, pages 201-204, Seattle, WA. Institute of Electrical and Electronics Engineers.Google Scholar
Matheson, C., Poesio, M., and Traum, D. (2000). Modelling grounding and discourse obligations using update rules. In Proceedings ofthe Conference ofthe North American Chapter of the Association for Computational Linguistics (NAACL), pages 1-8, Seattle, WA. Association for Computational Linguistics.Google Scholar
Nakano, Y. I., Reinstein, G., Stocky, T., and Cassell, J. (2003). Towards a model of face-to-face grounding. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pages 553-561, Sapporo, Japan. Association for Computational Linguistics.Google Scholar
Newell, A. (1982). The knowledge level. Artificial Intelligence, 18:87-127.CrossRefGoogle Scholar
Pollack, M. (1986). A model of plan inference that distinguishes between the beliefs of actors and observers. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pages 207-214, New York, NY. Association for Computational Linguistics.Google Scholar
Purver, M. (2004). The Theory and Use of Clarification Requests in Dialogue. PhD thesis, Department of Computer Science, King's College, University of London.Google Scholar
Rich, C., Sidner, C. L., and Lesh, N. (2001). COLLAGEN: Applying collaborative discourse theory to human-computer interaction. Artificial Intelligence Magazine, 22(4):15-25.Google Scholar
Rieser, V. and Lemon, O. (2011). Learning and evaluation of dialogue strategies for new applications: Empirical methods for optimization from small data sets. Computational Linguistics, 37(1):153-196.CrossRefGoogle Scholar
Roy, N., Pineau, J., and Thrun, S. (2000). Spoken dialog management for robots. In Proceedings of the Annual Meeting ofthe Association for Computational Linguistics (ACL), pages 93-100, Hong Kong. Association for Computational Linguistics.Google Scholar
Russell, S. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Prentice Hall, Upper Saddle River, NJ.Google Scholar
Sengers, P. (1999). Designing comprehensible agents. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 1227-1232, Stockholm, Sweden. International Joint Conference on Artificial Intelligence.Google Scholar
Sidner, C. L. (1994). Negotiation in collaborative activity: A discourse analysis. Knowledge Based Systems, 7(4):265-267.CrossRefGoogle Scholar
Stalnaker, R. (1974). Pragmatic presuppositions. In Munitz, M. K. and Unger, P. K., editors, Semantics and Philosophy, pages 197-213. New York University Press, New York, NY.Google Scholar
Stalnaker, R. (1978). Assertion. In Cole, P., editor, Syntax and Semantics, volume 9, pages 315-332. Academic Press, New York, NY.Google Scholar
Stent, A. J. (2002). A conversation acts model for generating spoken dialogue contributions. Computer Speech and Language, 16(3-4):313-352.CrossRefGoogle Scholar
Stone, M. (2004). Communicative intentions and conversational processes in human-human and human-computer dialogue. In Trueswell, J. C. and Tanenhaus, M. K., editors, Approaches to Studying World-Situated Language Use: Bridging the Language-as-Product and Language-as-Action Traditions, pages 39-70. MIT Press, Cambridge, MA.Google Scholar
Stone, M., Doran, C., Webber, B., Bleam, T., and Palmer, M. (2003). Microplanning with communicative intentions: The SPUD systems. Computational Intelligence, 19(4):314-381.CrossRefGoogle Scholar
Stone, M. and Lascarides, A. (2010). Coherence and rationality in dialogue. In Proceedings of the Workshop on the Semantics and Pragmatics of Dialogue (SEMDIAL), pages 51-58, Poznan, Poland. SemDial.Google Scholar
Stone, M. and Oh, I. (2008). Modeling facial expression of uncertainty in conversational animation. In Wachsmuth, I. and Knoblich, G., editors, Modeling Communication with Robots and Virtual Humans, pages 57-76. Springer, Heidelberg, Germany.Google Scholar
Swartout, W., Gratch, J., Hill, R. W., Hovy, E., Marsella, S., Rickel, J., and Traum, D. (2006). Toward virtual humans. AIMagazine, 27(2):96-108.Google Scholar
Swerts, M. and Krahmer, E. (2005). Audiovisual prosody and feeling of knowing. Journal of Memory and Language, 53(1):81-94.CrossRefGoogle Scholar
Tetreault, J. and Litman, D. (2006). Using reinforcement learning to build a better model of dialogue state. In Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL), pages 289-296, Trento, Italy. Association for Computational Linguistics.Google Scholar
Thomason, R. H., Stone, M., and DeVault, D. (2006). Enlightened update: A computational architecture for presupposition and other pragmatic phenomena. For the Ohio State Pragmatics Initiative, 2006, available at http://www.research.rutgers.edu/~ddevault/. Accessed on 11/24/2013.Google Scholar
Traum, D. and Allen, J. F. (1994). Discourse obligations in dialogue processing. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pages 1-8, Las Cruces, NM. Association for Computational Linguistics.Google Scholar
Traum, D. and Hinkelman, E. (1992). Conversation acts in task-oriented spoken dialogue. Computational Intelligence, 8(3):575-599.CrossRefGoogle Scholar
Traum, D. R. (1994). A Computational Theory ofGrounding in Natural Language Conversation. PhD thesis, Department of Computer Science, University of Rochester.Google Scholar
Wahlster, W., Reithinger, N., and Blocher, A. (2001). SmartKom: Multimodal communication with a life-like character. In Proceedings of the European Conference on Speech Communication and Technology (EUROSPEECH), pages 1547-1550, Aalborg, Denmark. International Speech Communication Association.Google Scholar
Walker, M. A. (2000). An application of reinforcement learning to dialogue strategy selection in a spoken dialogue system for email. Journal of Artificial Intelligence Research, 12:387-416.Google Scholar
Williams, J. and 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. AAAI Press.Google Scholar
Williams, J. D. (2008). Demonstration of a POMDP voice dialer. In Proceedings of the Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), pages 1-4, Columbus, OH. Association for Computational Linguistics.Google Scholar
Williams, J. D. and Young, S. (2007). Partially observable Markov decision processes for spoken dialog systems. Computer Speech and Language, 21(2):393-122.CrossRefGoogle Scholar

Accessibility standard: Unknown

Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×