Humor Utterance Generation for Non-task-oriented Dialogue Systems

Shohei Fujikura, Yoshito Ogawa, H. Kikuchi
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引用次数: 1

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.
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非任务导向对话系统的幽默话语生成
我们提出了一种与对话系统兼容的幽默话语生成方法,以增加“继续对话的欲望”。一个对话系统从Twitter中检索引导项:名词对作为知识,并尝试使用单词相似度来选择最幽默的回复,这表明不协调可以通过不协调解决模型来解释。我们考虑了个体之间的差异,并证实了所提出方法的有效性。实验结果表明,在有限条件下,高不一致性应答对低不一致性应答具有显著的有效性。
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