Motoyasu Fujita, Rafal Rzepka, and Kenji Araki
Natural Language Processing, Causal Knowledge, Utterance Generation
In this paper, we describe the effectiveness of utterance generation using causal knowledge for a dialogue system. Recently, there has been a variety of research on non-task-oriented dialogue systems; however, an effective approach has not yet been developed. One of the most important reasons for this is that non-task-oriented dialogue systems lack common sense knowledge, which is not in their databases. As the first step towards solving this problem, we concentrated on causal knowledge containing reasons and effects, which can provide unwritten meanings for utterance understanding and generating modules. In this paper we investigated how an utterance generated with knowledge related to user input can improve an existing conversational system. Experiment results show that utterance generation using causal knowledge can improve a conversational system.
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