A Classifier-Based Approach to Supporting the Augmentation of the Question-Answer Database for Spoken Dialogue Systems

Dealing with a variety of user questions in question-answer spoken dialogue systems requires preparing as many question-answer patterns as possible. This paper proposes a method for supporting the augmentation of the question-answer database. It uses user questions collected with an initial question-answer system, and detects questions that need to be added to the database. It uses two language models; one is built from the database and the other is a large-vocabulary domain-independent model. Experimental results suggest the proposed method is effective in reducing the amount of effort for augmenting the database when compared to a baseline method that used only the initial database.