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    <titleInfo>
        <title>Dialogue-Act Prediction of Future Responses Based on Conversation History</title>
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    <name type="personal">
        <namePart type="given">Koji</namePart>
        <namePart type="family">Tanaka</namePart>
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    <name type="personal">
        <namePart type="given">Junya</namePart>
        <namePart type="family">Takayama</namePart>
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    <name type="personal">
        <namePart type="given">Yuki</namePart>
        <namePart type="family">Arase</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
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        <name type="personal">
            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
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        <name type="personal">
            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
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    <abstract>Sequence-to-sequence models are a common approach to develop a chatbot. They can train a conversational model in an end-to-end manner. One significant drawback of such a neural network based approach is that the response generation process is a black-box, and how a specific response is generated is unclear. To tackle this problem, an interpretable response generation mechanism is desired. As a step toward this direction, we focus on dialogue-acts (DAs) that may provide insight to understand the response generation process. In particular, we propose a method to predict a DA of the next response based on the history of previous utterances and their DAs. Experiments using a Switch Board Dialogue Act corpus show that compared to the baseline considering only a single utterance, our model achieves 10.8% higher F1-score and 3.0% higher accuracy on DA prediction.</abstract>
    <identifier type="citekey">tanaka-etal-2019-dialogue</identifier>
    <identifier type="doi">10.18653/v1/P19-2027</identifier>
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        <date>2019-07</date>
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            <start>197</start>
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%0 Conference Proceedings
%T Dialogue-Act Prediction of Future Responses Based on Conversation History
%A Tanaka, Koji
%A Takayama, Junya
%A Arase, Yuki
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F tanaka-etal-2019-dialogue
%X Sequence-to-sequence models are a common approach to develop a chatbot. They can train a conversational model in an end-to-end manner. One significant drawback of such a neural network based approach is that the response generation process is a black-box, and how a specific response is generated is unclear. To tackle this problem, an interpretable response generation mechanism is desired. As a step toward this direction, we focus on dialogue-acts (DAs) that may provide insight to understand the response generation process. In particular, we propose a method to predict a DA of the next response based on the history of previous utterances and their DAs. Experiments using a Switch Board Dialogue Act corpus show that compared to the baseline considering only a single utterance, our model achieves 10.8% higher F1-score and 3.0% higher accuracy on DA prediction.
%R 10.18653/v1/P19-2027
%U https://aclanthology.org/P19-2027
%U https://doi.org/10.18653/v1/P19-2027
%P 197-202

Markdown (Informal)

[Dialogue-Act Prediction of Future Responses Based on Conversation History](https://aclanthology.org/P19-2027) (Tanaka et al., ACL 2019)

• Dialogue-Act Prediction of Future Responses Based on Conversation History (Tanaka et al., ACL 2019)

ACL

• Koji Tanaka, Junya Takayama, and Yuki Arase. 2019. Dialogue-Act Prediction of Future Responses Based on Conversation History. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 197–202, Florence, Italy. Association for Computational Linguistics.