When Your Users Are Not Serious  Using Web-based Associations, Affect and Humor for Generating Appropriate Utterances for Inappropriate Input

Rzepka, Rafał; Higuchi, Shinsuke; Ptaszyński, Michał; Dybała, Paweł; Araki, Kenji

doi:10.1527/tjsai.25.114

Cited by 2 publications

References 17 publications

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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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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

Self Cite

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Evaluation and Analysis of Fun of Dialogue Contained Puns for Construction of Pun Database with Dialogue Context

HANABUSA,

ARAKI

2023

J. SOFT

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There are more opportunities to interact with dialogue agents by the spread of smartphones and smart speakers. However, the dialogue agents in these devices lack the ability to entertain people. Dialogues with these agents are not as enjoyable as talking with a close friend. In order for dialogue agents to provide enjoyment to people, the system needs to recognize and generate humor from the context of the dialogue. At the moment, it is not clear whether humor that includes contextual information during dialogue is funny or how information affects the funny. Therefore, to construct a standard database for evaluating the fun of puns considering the dialogue context, we collected dialogues containing puns on Twitter. Using the collected dialogues, multiple people evaluated the fun. Moreover, we analyzed the characteristics of a dialogue context that affects the fun of puns.

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When Your Users Are Not Serious Using Web-based Associations, Affect and Humor for Generating Appropriate Utterances for Inappropriate Input

Cited by 2 publications

References 17 publications

Evaluation of Utterances based on Causal Knowledge Retrieved from Blogs

Evaluation of Utterances based on Causal Knowledge Retrieved from Blogs

Evaluation and Analysis of Fun of Dialogue Contained Puns for Construction of Pun Database with Dialogue Context

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