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        <title>Stance Detection with Hierarchical Attention Network</title>
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        <namePart type="given">Qingying</namePart>
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        <namePart type="given">Zhongqing</namePart>
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        <namePart type="given">Guodong</namePart>
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    <abstract>Stance detection aims to assign a stance label (for or against) to a post toward a specific target. Recently, there is a growing interest in using neural models to detect stance of documents. Most of these works model the sequence of words to learn document representation. However, much linguistic information, such as polarity and arguments of the document, is correlated with the stance of the document, and can inspire us to explore the stance. Hence, we present a neural model to fully employ various linguistic information to construct the document representation. In addition, since the influences of different linguistic information are different, we propose a hierarchical attention network to weigh the importance of various linguistic information, and learn the mutual attention between the document and the linguistic information. The experimental results on two datasets demonstrate the effectiveness of the proposed hierarchical attention neural model.</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Stance Detection with Hierarchical Attention Network
%A Sun, Qingying
%A Wang, Zhongqing
%A Zhu, Qiaoming
%A Zhou, Guodong
%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 sun-etal-2018-stance
%X Stance detection aims to assign a stance label (for or against) to a post toward a specific target. Recently, there is a growing interest in using neural models to detect stance of documents. Most of these works model the sequence of words to learn document representation. However, much linguistic information, such as polarity and arguments of the document, is correlated with the stance of the document, and can inspire us to explore the stance. Hence, we present a neural model to fully employ various linguistic information to construct the document representation. In addition, since the influences of different linguistic information are different, we propose a hierarchical attention network to weigh the importance of various linguistic information, and learn the mutual attention between the document and the linguistic information. The experimental results on two datasets demonstrate the effectiveness of the proposed hierarchical attention neural model.
%U https://aclanthology.org/C18-1203
%P 2399-2409

Markdown (Informal)

[Stance Detection with Hierarchical Attention Network](https://aclanthology.org/C18-1203) (Sun et al., COLING 2018)

• Stance Detection with Hierarchical Attention Network (Sun et al., COLING 2018)

ACL

• Qingying Sun, Zhongqing Wang, Qiaoming Zhu, and Guodong Zhou. 2018. Stance Detection with Hierarchical Attention Network. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2399–2409, Santa Fe, New Mexico, USA. Association for Computational Linguistics.