An Analysis of 'Distinctive' Utterances in Non-task-oriented Conversational Dialogue

Tokuhisa, Ryoko; Terashima, Ryuta

doi:10.1527/tjsai.22.425

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…To resolve this problem, we started with the automatic addition of causal knowledge. Because such knowledge includes cause and effect relationships, we presumed that a conversational system should use this to guess the relationship between the users input and the system's world knowledge that might be used for an elaborative response, which is proven to be better than a simple one [4]. The following utterances are an example of a dialogue using cause-effect knowledge.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Utterances based on Causal Knowledge Retrieved from Blogs

Fujita¹,

Rzepka

Araki

2011

Signal and Image Processing and Applications / 716: Artificial Intelligence and Soft Computing

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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-taskoriented 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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Section: Introductionmentioning

confidence: 99%