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        <title>Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts</title>
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        <namePart type="given">Yasuhide</namePart>
        <namePart type="family">Miura</namePart>
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        <namePart type="given">Ryuji</namePart>
        <namePart type="family">Kano</namePart>
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    <name type="personal">
        <namePart type="given">Motoki</namePart>
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    <name type="personal">
        <namePart type="given">Tomoki</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Tomoko</namePart>
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    <abstract>We proposed a model that integrates discussion structures with neural networks to classify discourse acts. Several attempts have been made in earlier works to analyze texts that are used in various discussions. The importance of discussion structures has been explored in those works but their methods required a sophisticated design to combine structural features with a classifier. Our model introduces tree learning approaches and a graph learning approach to directly capture discussion structures without structural features. In an evaluation to classify discussion discourse acts in Reddit, the model achieved improvements of 1.5% in accuracy and 2.2 in FB1 score compared to the previous best model. We further analyzed the model using an attention mechanism to inspect interactions among different learning approaches.</abstract>
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%0 Conference Proceedings
%T Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts
%A Miura, Yasuhide
%A Kano, Ryuji
%A Taniguchi, Motoki
%A Taniguchi, Tomoki
%A Misawa, Shotaro
%A Ohkuma, Tomoko
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F miura-etal-2018-integrating
%X We proposed a model that integrates discussion structures with neural networks to classify discourse acts. Several attempts have been made in earlier works to analyze texts that are used in various discussions. The importance of discussion structures has been explored in those works but their methods required a sophisticated design to combine structural features with a classifier. Our model introduces tree learning approaches and a graph learning approach to directly capture discussion structures without structural features. In an evaluation to classify discussion discourse acts in Reddit, the model achieved improvements of 1.5% in accuracy and 2.2 in FB1 score compared to the previous best model. We further analyzed the model using an attention mechanism to inspect interactions among different learning approaches.
%U https://aclanthology.org/C18-1322
%P 3806-3818

Markdown (Informal)

[Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts](https://aclanthology.org/C18-1322) (Miura et al., COLING 2018)

• Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts (Miura et al., COLING 2018)

ACL

• Yasuhide Miura, Ryuji Kano, Motoki Taniguchi, Tomoki Taniguchi, Shotaro Misawa, and Tomoko Ohkuma. 2018. Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3806–3818, Santa Fe, New Mexico, USA. Association for Computational Linguistics.