Hostname: page-component-cb9f654ff-9knnw Total loading time: 0 Render date: 2025-08-19T16:50:51.509Z Has data issue: false hasContentIssue false
  • English
  • Français

Special issue on interactive question answering: Introduction

Published online by Cambridge University Press:  01 January 2009

N. WEBB  and
B. WEBBER
N. WEBB
Affiliation:
Institute of Informatics, Logics and Security Studies, University at Albany, SUNY, USA e-mail: nwebb@albany.edu
B. WEBBER
Affiliation:
School of Informatics, University of Edinburgh, UK e-mail: bonnie@inf.ed.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

In this introduction, we present our overview of interactive question answering (IQA). We contextualize IQA in the wider field of question answering, and establish connections to research in Information Retrieval and Dialogue Systems. We highlight the development of QA as a field, and identify challenges in the present research paradigm for which IQA is a potential solution. Finally, we present an overview of papers in this special issue, drawing connections between these and the challenges they address.

Information

Type
Introduction
Information
Natural Language Engineering , Volume 15 , Special Issue 1: Interactive Question Answering , January 2009 , pp. 1 - 8
Copyright
Copyright © Cambridge University Press 2008

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.)

Article purchase

Temporarily unavailable

References

Allen, J., Miller, B., Ringger, E. and Sikorski, T. 1996. A robust system for natural spoken dialogue. In Proceedings of the 34th Annual Meeting, Association for Computational Linguistics, University of California, Santa Cruz, pp. 62–70.Google Scholar
Bouma, G., Fahmi, I., Mur, J., van Noord, G., van der Plas, L. and Tiedemann, J. 2005. Linguistic knowledge and qa. Traitement Automatique des Langues (TAL) 46 (3):15–39.Google Scholar
Brown, J. S. and Burton, R. 1975. Multiple representations of knowledge for tutorial reasoning. In Bobrow, D. G. and Collins, A. (eds.), Representation and Understanding, pp. 311–49. New York: Academic Press.CrossRefGoogle Scholar
Chu-Carroll, J. and Carpenter, B. 1999. Vector-based natural language call routing. Computational Linguistics 25: 361–88.Google Scholar
De Roeck, A., Kruschwitz, U., Scott, P., Steel, S., Turner, R. and Webb, N. 2000. The YPA - an assistant for classified directory enquiry. In Intelligent Systems and Soft Computing: Prospects, Tools and Applications. Lecture Notes in Artificial Intelligence (LNAI), Berlin, Germany, vol. 1804. Springer Verlag.CrossRefGoogle Scholar
Demberg, V. and Moore, J. 2006. Information presentation in spoken dialogue systems. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Trento, Italy.Google Scholar
Dumais, S. T. and Belkin, N. J. 2005. The TREC interactive tracks: putting the user into search. In Voorhees, E. M., and Harman, D. K., (eds.), TREC: Experiment and Evaluation in Information Retrieval, Cambridge, MA, USA, pp. 123–53. MIT Press.Google Scholar
Eliasson, K. 2007. Case-based techniques used for dialogue understanding and planning in a human-robot dialogue system. In Proceedings of the International Joint Conference on Artificial Intelligence, Hyderabad, India, pp. 1600–05.Google Scholar
Gorin, A., Riccardi, G. and Wright, J. 1997. How May I Help You? Speech Communication 23: 113–27.CrossRefGoogle Scholar
Green, B. F., Wolf, A. K., Chomsky, C. and Laughery, K. 1961. Baseball: an automatic question answerer. In Proceedings of the Western Joint Computer Conference, NY, USA, pp. 219–224.Google Scholar
Hardy, H., Biermann, A., Inouye, R. B., Mckenzie, A., Strzalkowski, T., Ursu, C., Webb, N. and Wu, M. 2004. Data driven strategies for an automated dialogue system. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004), Barcelona.CrossRefGoogle Scholar
Hemphill, C., Godfrey, J. and Doddington, G. 1990. The ATIS spoken language systems pilot corpus. In Proceedings of the DARPA Speech and Natural Language Workshop, Hidden Valley, PA, USA, pp. 96–101.Google Scholar
Henderson, J., Lemon, O. and Georgila, K. 2008. Hybrid reinforcement/supervised learning of dialogue policies from fixed datasets. Computational Linguistics, 34, to appear.CrossRefGoogle Scholar
Hendrix, G. 1986. Bringing natural language processing to the microcomputer market. In Proceedings of the 24th Annual Meeting of the Association for Computational Linguistics, NY, USA.CrossRefGoogle Scholar
Hendrix, G., Sacerdoti, E., Sagalowicz, D. and Slocum, J. 1978. Developing a natural language interface to complex data. ACM Transactions on Database Systems 3: 105–47.CrossRefGoogle Scholar
Kaisser, M. and Webber, B. June 2007. Question answering based on semantic roles. In ACL 2007 Workshop on Deep Linguistic Processing, pp. 41–48, Prague, Czech Republic. Association for Computational Linguistics.CrossRefGoogle Scholar
Kaplan, J. 1982. Cooperative responses from a portable natural language database query system. In Brady, M. and Berwick, R. (eds.), Computational Models of Discourse, pp. 167–208. Cambridge MA: MIT Press.Google Scholar
Lehtinen, G., Safra, S., Gauger, M., Kaspar, B., Pardo, J. M. and Louloudis, D. 2000. Idas: interactive directory assistance services. In Proceedings of the COST249 ISCA Workshop on Voice Operated Telecom Services Ghent, Belgium, pp. 51–54.Google Scholar
Lemon, O., Bracy, A., Gruenstein, A. and Peters, S. 2001. The witas multi-modal dialogue system i. In Proceedings of 7th European Conference on Speech Communication and Technology (eurospeech), Aalborg, Denmark.CrossRefGoogle Scholar
Litman, D. and Forbes-Riley, K. 2006. Correlations betweeen dialogue acts and learning in spoken tutoring dialogues. Natural Language Engineering 12: 161–76. Further information at http://www.cs.pitt.edu/litman/itspoke.html.CrossRefGoogle Scholar
Mays, E., Joshi, A. and Webber, B. 1982. Taking the initiative in natural language data base interactions: monitoring as response. In Proceedings of the European Conference on Artificial Intelligence Orsay, France, pp. 255–56.Google Scholar
Pollack, M. 1986. Inferring Domain Plans in Question-Answering. Ph.D. Thesis, Department of Computer & Information Science, University of Pennsylvania.Google Scholar
Seneff, S. 2002. Response planning and generation in the mercury flight reservation system. Computer Speech and Language 16: 283–312.CrossRefGoogle Scholar
Traum, D. and Larsson, S. 2003. The information state approach to dialogue management. In vanKuppevelt, J., and Smith, R. (eds.), Current and New Directions in Discourse and Dialogue, Berlin, Germany, pp. 325–53. Kluwer.CrossRefGoogle Scholar
Vere, S. and Bickmore, T. 1990. A basic agent. Computational Intelligence 6 (1): 41–60.CrossRefGoogle Scholar
Walker, M., Passonneau, R. and Boland, J. E. 2001. Quantitative and qualitative evaluation of darpa communicator spoken dialogue systems. In Proceedings of the Meeting of the Association of Computational Linguistics.CrossRefGoogle Scholar
Walker, M., Whittaker, S. and Stent, A. 2004. Generation and evaluation of user tailored responses in dialogue. Cognitive Science 28: 811–40.CrossRefGoogle Scholar
Webber, B. 1986. Questions, answers and responses. In Brodie, M. and Mylopoulos, J. (eds.), On Knowledge Base Systems, pp. 365–401. New York: Springer-Verlag.CrossRefGoogle Scholar
Winograd, T. 1973. A procedural model of language understanding. In Schank, R. and Colby, K. (eds.), Computer Models of Thought and Language, pp. 152–86. New York: W. H. Freeman and Company. Reprinted in B. J. Grosz, K. Spark-Jones and B. L. Webber (eds.). 1986. Readings in Natural Language Processing, pp. 249–66. Los Altos CA: Morgan Kaufmann.Google Scholar
Woods, W., Kaplan, R. and Nash-Webber, B. 1972. The Lunar Sciences Natural Language Information System: Final Report. In BBN Report 2378.Google Scholar