Abstract
It is difficult to determine the cause of a breakdown in a dialogue when using a chat-oriented dialogue system due to a wide variety of possible causes. To address this problem, we analyzed a chat dialogue corpus and formulated a taxonomy of the errors that could lead to dialogue breakdowns. The experimental results demonstrated the effectiveness of the taxonomy to some degree. We also developed a breakdown detector that comprises combinations of classifiers for different causes of errors based on the taxonomy.
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Acknowledgements
We thank all members of the dialogue task in “Project Next NLP” for data collection, annotation, and productive discussions.
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Horii, T., Mori, H., Araki, M. (2017). Breakdown Detector for Chat-Oriented Dialogue. In: Jokinen, K., Wilcock, G. (eds) Dialogues with Social Robots. Lecture Notes in Electrical Engineering, vol 427. Springer, Singapore. https://doi.org/10.1007/978-981-10-2585-3_9
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DOI: https://doi.org/10.1007/978-981-10-2585-3_9
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