Analyzing interactive QA dialogues using logistic regression models

Abstract

With traditional Question Answering (QA) systems having reached nearly satisfactory performance, an emerging challenge is the development of successful Interactive Question Answering (IQA) systems. Important IQA subtasks are the identification of a dialogue-dependent typology of Follow Up Questions (FU Qs), automatic detection of the identified types, and the development of different context fusion strategies for each type. In this paper, we show how a system relying on shallow cues to similarity between utterances in a narrow dialogue context and other simple information sources, embedded in a machine learning framework, can improve FU Q answering performance by implicitly detecting different FU Q types and learning different context fusion strategies to help re-ranking their candidate answers. © Springer-Verlag 2009.