System Utterance Generation by Label Propagation over Association Graph of Words and Utterance Patterns for Open-Domain Dialogue Systems

A novel graph-based utterance generation method for open-domain dialogue systems is proposed in this paper. After an association graph of words and utterance patterns from a dialogue corpus is constructed, a label propagation algorithm is used for generating system utterances from the words and utterance patterns in the association graph that are found to strongly correlate with the words and utterance patterns that appeared in previous user utterances. We also propose a crowdsourcing framework for collecting annotated chat data so that we can implement our method in a cost effective manner. Crowdsourcing is also used for conducting subjective evaluations and the results will show that the proposed method can not only provide interesting and informative responses but it also can appropriately expand the topics by comparing them to a well-known chat system in Japanese.