Computer Science and Engineering

Discourse Processing in Conversational QA

Supported by DTO/IARPA

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)

Selected Papers: