著者

稲葉 通将 神園 彩香 高橋 健一

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.29, no.1, pp.21-31, 2014-01-05 (Released:2014-01-07)

参考文献数

25

被引用文献数

5 9

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Recently, computerized dialogue systems are studied actively. Non-task-oriented dialogue systems that handle domain-free dialogues like chats are expected be applied in various fields, but many challenges still exist in developing them. This paper addresses the problem of utterance generation for non-task-oriented dialogue systems. We search twitter data by topic words and acquire sentences. The sentences are filtered by rules and scored on the basis of training data. We acquire the sentences which have a high score as utterances. The results of an experiment demonstrate that the proposed method can generate appropriate utterances with a high degree of accuracy.

著者

稲葉 通将 神園 彩香 高橋 健一

出版者

人工知能学会

雑誌

人工知能学会全国大会論文集 (ISSN:13479881)

巻号頁・発行日

vol.27, 2013

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人間と雑談を行う非タスク指向型対話システムは,様々な話題に柔軟に対応できることが求められる.そこで本研究では,Twitterデータを用いて発話を自動生成する手法を提案する.Twitterは大量にデータが取得できるものの,ノイズも極めて多いという欠点がある.本研究では,ツイートに点数付けすることでノイズを排除し,対話に利用可能な発話を自動生成する手法を提案する.

著者

稲葉 通将 神園 彩香 高橋 健一

出版者

The Japanese Society for Artificial Intelligence

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.29, no.1, pp.21-31, 2014

被引用文献数

9

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Recently, computerized dialogue systems are studied actively. Non-task-oriented dialogue systems that handle domain-free dialogues like chats are expected be applied in various fields, but many challenges still exist in developing them. This paper addresses the problem of utterance generation for non-task-oriented dialogue systems. We search twitter data by topic words and acquire sentences. The sentences are filtered by rules and scored on the basis of training data. We acquire the sentences which have a high score as utterances. The results of an experiment demonstrate that the proposed method can generate appropriate utterances with a high degree of accuracy.