(Semi-)Automatic Analysis of Dialogues

Mare Koit

2014

Abstract

We study human-human and human-computer dialogues with the aim to determine which dialogue acts and communicative strategies do the participants of interaction use, and which structural parts does a dialogue include. We develop software that makes it possible to recognise and annotate the dialogue acts, the dialogue structure and the communicative strategies. In order to recognise dialogue acts, a data-driven method is implemented when determination of the dialogue structure and the strategies is based on rules. The software tool is used by linguists in dialogue studies which further aim is to develop a dialogue system that interacts with a user in natural language following norms and rules of human-human communication. The contribution of the paper consists of integration of the existing approaches within a common platform and adaptation to the Estonian language.

References

  1. Allen, J., Core, M. 1997. Draft of DAMSL: Dialog Act Markup in Several Layers http:// www.cs.rochester.edu/research/cisd/resources/damsl/R evisedManual/RevisedManual.html.
  2. Aller, S. 2012. Dialoogiaktide märgendamine Eesti dialoogikorpuses: ülevaade ressurssidest ja tarkvaraarendus. [Recognition of Dialogue Acts in the Estonian Dialogue Corpus: Overview of Resources and Software Development.] Master's thesis. University of Tartu. http://comserv.cs.ut.ee/ forms/ati_report/
  3. Bellucci, A., Bottoni, P., Levialdi, S. 2009. WOEB: Rapid Setting of Wizard of Oz Experiments and Reuse for Deployed Applications. Dipartimento di Informatica, Università Sapienza di Roma, Italy.
  4. Bunt, H., Alexandersson, J., Carletta, J., Choe, J.-W., Chengyu Fang, A., Hasida, K., Lee, K., Petukhova, V., Popescu-Belis, A., Romary, L., Soria, C., Traum, D.R. 2012. ISO 24617-2: A semantically-based standard for dialogue annotation. In Proc. of LREC-2012, European Language Resources Association (ELRA), Istanbul, Turkey, 430-437.
  5. Daelemans, W., Zavrel, J., van der Sloot, K., van den Bosch, A. 2004. TiMBL: Tilburg Memory-Based Learner Reference Guide. Technical Report ILK 04- 02. Tilburg University and University of Antwerp.
  6. Dahlbäck, N., Jönsson, A., Ahrenberg, L. 1993. Wizard of Oz studies: why and how. In Knowledge-Based Systems, 6, 4, 258-266. doi:10.1016/0950- 7051(93)90017-N.
  7. Fernandez, R., Ginzburg, J., Lappin, S. 2005. Using Machine Learning for Non-Sentential Utterance Classification. In Proceedings of the 6th SIGdial Workshop on Discourse and Dialogue. Lisbon, Portugal, 77-86.
  8. Field, D., Worgan, S., Webb, N., Wilks, Y. 2008. Automatic Induction of Dialogue Structure from the Companions Dialogue Corpus. In Proc. of the 4th International Workshop on Human-Computer Conversation, Bellagio, Italy.
  9. Georgila, K, Artstein, R., Nazarian, A., Rushforth, M., Traum, D.R., Sycara, K. 2011. An annotation scheme for cross-cultural argumentation and persuasion dialogues. In 12th Annual SIGdial Meeting on Discourse and Dialogue. Portland, Oregon, USA, 272- 278.
  10. Fishel, M. 2007. Complex Taxonomy Dialogue Act Recognition with a Bayesian Classifier. In Proceedings: DECALOG'2007 Workshop on the Semantics and Pragmatics of Dialogue. Rovereto, Italy, 161-162.
  11. Hennoste, T., Rääbis, A. 2004. Dialoogiaktid eesti infodialoogides: tüpoloogia ja analüüs. [Dialogue acts in Estonian information dialogues: a typology and analysis.] Tartu: TÜ Kirjastus. http://dspace.utlib.ee/dspace/handle/10062/18995.
  12. Hutchby, I., Wooffitt, R. 1998. Conversation Analysis. Principles, Practices and Applications. Cambridge, UK: Polity Press.
  13. Jokinen, K. 2009. Constructive Dialogue Modelling: Speech Interaction and Rational Agents. John Wiley & Sons Ltd.
  14. Jokinen, K. 1996. Cooperative Response Planning in CDM: Reasoning about Communicative Strategies. In TWLT11. Dialogue Management in Natural Language Systems, S. LuperFoy, A. Nijholt, G. Veldhuijzen van Zanten, ed. Enschede: Universiteit Twente, 159-168.
  15. Keizer, S., Op den Akker, R., Nijholt, A. 2002. Dialogue Act Recognition with Bayesian Networks for Dutch Dialogues. In Proceedings of the 3rd SIGdial Workshop on Discourse and Dialogue. Philadelphia, USA, 88-94.
  16. Koit, M. 2012. Towards automatic recognition of the structure of Estonian directory inquiries. In Proc. of 5th Int. Conf. on Human Language Technologies: the Baltic Perspective: Baltic HLT 2012, Tartu, Oct. 2012. (Ed.) A. Tavast, K. Muischnek, M. Koit. IOS Press, 2012, 120- 128.
  17. Koit, M. 2011. Automatic Recognition of Dialogue Acts in Complex Typology. In Proc. of INISTA: International Symposium on INnovations in Intelligent SysTems and Applications, Istanbul. (Ed.) Akyokus, S. et al.. Istanbul: IEEE, 2011, 485-489.
  18. Koit, M. 2003. The structure of information dialogues: a case study. In 10th International Conference Knowledge-Dialogue-Solution. Proceedings: 10th International Conference Knowledge-DialogueSolution, Varna, Bulgaria. Sofia: FOI-COMMERCE, 2003, 307-314.
  19. Levin, L., Ries, K., Thyme-Gobbel, A., Levie, A. 1999. Tagging of Speech Acts and Dialogue Games in Spanish Call Home. In Proceedings of the ACL Workshop “Towards Standards and Tools for Discourse Tagging”. Somerset, NJ, USA, 42-47.
  20. Manning, C.D., Schütze, H. 1999. Foundations of Statistical Natural Language Processing. MIT Press.
  21. Reithinger, N., Maier, E. 1995. Utilizing Statistical Dialogue Act Processing in VERBMOBIL. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics. Cambridge, Massachusetts, 116-121.
  22. Sinclair, J., Coulthard, M. 1975. Towards an Analysis of Discourse. Oxford:Oxford University Press.
  23. Stenström, A.-B. 1994. An Introduction to Spoken Interaction. London and New York: Longman.
  24. Wright, H., Poesio, M., Isard, S. 1999. Using High Level Dialogue Information for Dialogue Act Recognition Using Prosodic Features. In Proceedings of an ESCA Tutorial and Research Workshop on Dialogue and Prosody. Eindhoven, The Netherlands, 139-143.
  25. 5. Repairs initiated and made by different participants, e.g. RPF: Non-understanding, RPS: Repair.
  26. 6. Directives and grants (request, proposal, offer, etc.), e.g. DIF: Request, DIS: Giving information.
  27. 7. Questions and answers, e.g. QUF: Closed yes/no, QUS: Yes, QUS: No.
  28. 8. Opinions and responses (assertion, etc.), e.g. OPF: Assertion, OPS: Accept, OPS: Reject.
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Paper Citation


in Harvard Style

Koit M. (2014). (Semi-)Automatic Analysis of Dialogues . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 445-452. DOI: 10.5220/0004818104450452


in Bibtex Style

@conference{icaart14,
author={Mare Koit},
title={(Semi-)Automatic Analysis of Dialogues},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={445-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004818104450452},
isbn={978-989-758-015-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - (Semi-)Automatic Analysis of Dialogues
SN - 978-989-758-015-4
AU - Koit M.
PY - 2014
SP - 445
EP - 452
DO - 10.5220/0004818104450452