Deep analysis in IQA: evaluation on real users dialogues.

Abstract

Interactive Question Answering (IQA) is a natural and cohesive way for a user to obtain information by interactive with a system using natural language. With the advancement in Natural Language Processing, research in the eld of IQA has started to focus on the role of semantics and the discourse structure in these systems. The need for a deeper analysis, which examines the syntax and semantics of the questions and the answers is evident. Using this deeper analysis allows us to model the context of the interaction. I will look at a current closeddomain IQA system which is based on Linear Regression modeling. This system uses super cial and non-semantically motivated features. I propose adding deep analysis and semantic features in order to improve the system and show the need for such analysis. Particular attention will be placed on the so-called follow-up questions (questions that the user poses after having received some answer from the system) and the role of context. I propose that adding the linguistically heavy features will prove bene cial, thereby showing the need for such analysis in IQA systems.