Koh Mitsuda


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Investigating person-specific errors in chat-oriented dialogue systems
Koh Mitsuda | Ryuichiro Higashinaka | Tingxuan Li | Sen Yoshida
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Creating chatbots to behave like real people is important in terms of believability. Errors in general chatbots and chatbots that follow a rough persona have been studied, but those in chatbots that behave like real people have not been thoroughly investigated. We collected a large amount of user interactions of a generation-based chatbot trained from large-scale dialogue data of a specific character, i.e., target person, and analyzed errors related to that person. We found that person-specific errors can be divided into two types: errors in attributes and those in relations, each of which can be divided into two levels: self and other. The correspondence with an existing taxonomy of errors was also investigated, and person-specific errors that should be addressed in the future were clarified.


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Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs
Takashi Kodama | Ryuichiro Higashinaka | Koh Mitsuda | Ryo Masumura | Yushi Aono | Ryuta Nakamura | Noritake Adachi | Hidetoshi Kawabata
Proceedings of the 12th Language Resources and Evaluation Conference

This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating utterances reflecting the meta information, especially emotion.


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Investigating the Effect of Conveying Understanding Results in Chat-Oriented Dialogue Systems
Koh Mitsuda | Ryuichiro Higashinaka | Junji Tomita
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

In dialogue systems, conveying understanding results of user utterances is important because it enables users to feel understood by the system. However, it is not clear what types of understanding results should be conveyed to users; some utterances may be offensive and some may be too commonsensical. In this paper, we explored the effect of conveying understanding results of user utterances in a chat-oriented dialogue system by an experiment using human subjects. As a result, we found that only certain types of understanding results, such as those related to a user’s permanent state, are effective to improve user satisfaction. This paper clarifies the types of understanding results that can be safely uttered by a system.


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Annotation for annotation - Toward eliciting implicit linguistic knowledge through annotation - (Project Note)
Takenobu Tokunaga | Ryu Iida | Koh Mitsuda
Proceedings of the 9th Joint ISO - ACL SIGSEM Workshop on Interoperable Semantic Annotation

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Investigation of annotator’s behaviour using eye-tracking data
Ryu Iida | Koh Mitsuda | Takenobu Tokunaga
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse

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Detecting Missing Annotation Disagreement using Eye Gaze Information
Koh Mitsuda | Ryu Iida | Takenobu Tokunaga
Proceedings of the 11th Workshop on Asian Language Resources