Humor Utterance Generation for Non-task-oriented Dialogue Systems

Abstract

We propose a humor utterance generation method that is compatible with dialogue systems, to increase "desire of continuing dialogue". A dialogue system retrieves leading-item:noun pairs from Twitter as knowledge and attempts to select the most humorous reply using word similarity, which reveals that incongruity can be explained by the incongruity-resolution model. We consider the differences among individuals, and confirm the validity of the proposed method. Experimental results indicate that high-incongruity replies are significantly effective against low-incongruity replies with a limited condition.