Adaptive selection from multiple response candidates in example-based dialogue

Masahiro Mizukami, Hideaki Kizuki, Toshio Nomura, Graham Neubig, Koichiro Yoshino, Sakriani Sakti, Tomoki Toda, Satoshi Nakamura

Abstract: In spoken dialogue systems, dialogue modeling is one of the most important factors for contributing to user satisfaction improvement. Especially in Example-Based Dialogue Modeling (EBDM), effective methods to build dialogue example databases and to select response utterances from examples are the keys for improving dialogue quality. In dialogue corpora, it often have plural appropriate responses for one utterance. However, the system merges these plural appropriate responses into the one system response, thus, it does not try to use plural responses properly by user preference. In fact, responses that each user thinks to be preferable are different. In this paper, we propose a framework that select an appropriate response from plural appropriate response candidates satisfies users. It has a multi-response example database, and selects an appropriate response based on collaborative filtering. Experimental results showed that the proposed framework were successfully choosing appropriate responses, considering multi-response candidates improves user satisfaction to 4.1 from 3.7 of single response, and the adaptive response selection method increased user satisfaction from 3.7 to 4.3.