Response Generation to Out-of-Database Questions for Example-Based Dialogue Systems

Sota Isonishi, Koji Inoue, Divesh Lala, Katsuya Takanashi, Tatsuya Kawahara

Abstract: Example-based dialogue systems are often used in practice because of their robustness and simple architecture. However, when these systems are given out-of-database questions that are not registered in the question-response database, they have to respond with a fixed backup response, which can make users disengaged in the dialogue. In this study, we address response generation for out-of-database questions to make users perceive that the system understands the question itself. We define question types observed in the speed-dating scenario which is based on open-domain dialogue. Then we define possible response frames for each question type. We propose a sequence-to-sequence model that directly generates an appropriate response frame from an input question sentence in an end-to-end manner. The proposed model also explicitly integrates a question type classification to take into account the question type of the out-of-database question. Experimental results show that integrating the question type classification improved the response generation, and could exactly match 69.2% of response frames provided by human annotators.