Towards intelligent QA interfaces

Abstract

Question answering (QA) systems take users' natural language questions and retrieve relevant answers from large repositories of free texts. Despite recent progress in QA research, most work on question answering is still focused on isolated questions. In a real-world information seeking scenario, questions are not asked in isolation, but rather in a coherent manner that involves a sequence of related questions to meet users' information needs. Therefore, to support coherent information seeking, intelligent QA interfaces will inevitably require techniques to support context question answering. To address this problem, this paper investigates approaches to discourse processing of a sequence of coherent questions and their implications on query expansion. In particular, we examine three models for query expansion that are motivated by Centering Theory. Our empirical results indicate that more sophisticated processing based on discourse transitions and centers can significantly improve the performance of document retrieval compared to models that only resolve references.