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        <title>Structured Representation Learning for Online Debate Stance Prediction</title>
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        <namePart type="given">Chang</namePart>
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    <name type="personal">
        <namePart type="given">Dan</namePart>
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            <namePart type="given">Emily</namePart>
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    <abstract>Online debates can help provide valuable information about various perspectives on a wide range of issues. However, understanding the stances expressed in these debates is a highly challenging task, which requires modeling both textual content and users’ conversational interactions. Current approaches take a collective classification approach, which ignores the relationships between different debate topics. In this work, we suggest to view this task as a representation learning problem, and embed the text and authors jointly based on their interactions. We evaluate our model over the Internet Argumentation Corpus, and compare different approaches for structural information embedding. Experimental results show that our model can achieve significantly better results compared to previous competitive models.</abstract>
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%0 Conference Proceedings
%T Structured Representation Learning for Online Debate Stance Prediction
%A Li, Chang
%A Porco, Aldo
%A Goldwasser, Dan
%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 li-etal-2018-structured
%X Online debates can help provide valuable information about various perspectives on a wide range of issues. However, understanding the stances expressed in these debates is a highly challenging task, which requires modeling both textual content and users’ conversational interactions. Current approaches take a collective classification approach, which ignores the relationships between different debate topics. In this work, we suggest to view this task as a representation learning problem, and embed the text and authors jointly based on their interactions. We evaluate our model over the Internet Argumentation Corpus, and compare different approaches for structural information embedding. Experimental results show that our model can achieve significantly better results compared to previous competitive models.
%U https://aclanthology.org/C18-1316
%P 3728-3739

Markdown (Informal)

[Structured Representation Learning for Online Debate Stance Prediction](https://aclanthology.org/C18-1316) (Li et al., COLING 2018)

• Structured Representation Learning for Online Debate Stance Prediction (Li et al., COLING 2018)

ACL

• Chang Li, Aldo Porco, and Dan Goldwasser. 2018. Structured Representation Learning for Online Debate Stance Prediction. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3728–3739, Santa Fe, New Mexico, USA. Association for Computational Linguistics.