Discourse Processing in Conversational QA
Motivations and Objectives
Question answering (QA) systems take users' natural language questions and automatically locate answers from large collections of documents. During the interactive QA, user questions are not only guided by users' information goals, but are also influenced by system responses. User information needs are gradually evolved as the QA session proceeds. Thus it is important to keep track of the interaction context and use the context to interpret user information needs, retrieve relevant information, and control the interaction. Therefore, this project aims to conduct a systematic investigation on how to represent interaction context (i.e., discourse), how to achieve such representation automatically, and how to effectively use the discourse representation in answer retrieval and dialog management. The systematic studies will help identify the appropriate level of discourse representation that will maximize the tradeoff between the impact and limitations of discourse modeling for conversational QA. Supported by DTO.
(Picture: Ph.D. student Matt Gerber demonstrates an interactive QA system, courtesy of GLITR)
Related Papers
- Michigan State University at the 2007 TREC ciQA Task. C. Zhang, M. Gerber, T. Baldwin, S. Emelander, J. Y. Chai, R. Jin. TREC 2007.
- Discourse Processing for Context Question Answering based on Linguistic Knowledge. M. Sun and J. Chai. Knowledge-based Systems, Volume 20, Issue 6, pp. 511-526, August 2007.
- Towards Conversational QA: Automated Identification of Problematic Situations and User Intent. J. Chai, C. Zhang, and T. Baldwin. Proceedings of the International Conference on Computational Linguistics/Association for Computational Linguistics (COLING/ACL-06) Poster Session, pp. 57-64. Sydney, Australia , July 17-21, 2006.
- Automated Performance Assessment in Interactive Question Answering. J. Chai, T. Baldwin, and C. Zhang. Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development on Information Retrieval (SIGIR2006), pp. 631-632. Seattle, USA, August 6-11, 2006.
- Discourse Structure for Context Question Answering. J. Chai and R. Jin. Proceedings of HLT-NAACL 2004 Workshop on Pragmatics in Question Answering, Boston, MA. May 3-7, 2004.