雑談システムにおけるTwitterデータからの統計的バックチャネル応答抽出手法

抄録

The backchannel plays an important role in smooth communication. For dialogue system, appropriate backchanneling is a significant factor that makes more natural conversation. However, many existing dialogue systems have poor backchannel patterns and only can produce simple responses. In this paper, we propose a method to extract various backchannels that are suitable for user utterance with no restriction of the diversity of backchannels. We conduct an experiment that compares the proposed method with two existing methods; a classification-based method and a simple extraction-based method with a message length limit. The generated responses are evaluated by human workers. The result shows that the proposed method generates backchannels that are highly diverse and more appropriate in terms of the response to the user utterance.