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        <title>Recognising Agreement and Disagreement between Stances with Reason Comparing Networks</title>
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    <abstract>We identify agreement and disagreement between utterances that express stances towards a topic of discussion. Existing methods focus mainly on conversational settings, where dialogic features are used for (dis)agreement inference. We extend this scope and seek to detect stance (dis)agreement in a broader setting, where independent stance-bearing utterances, which prevail in many stance corpora and real-world scenarios, are compared. To cope with such non-dialogic utterances, we find that the reasons uttered to back up a specific stance can help predict stance (dis)agreements. We propose a reason comparing network (RCN) to leverage reason information for stance comparison. Empirical results on a well-known stance corpus show that our method can discover useful reason information, enabling it to outperform several baselines in stance (dis)agreement detection.</abstract>
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%0 Conference Proceedings
%T Recognising Agreement and Disagreement between Stances with Reason Comparing Networks
%A Xu, Chang
%A Paris, Cecile
%A Nepal, Surya
%A Sparks, Ross
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F xu-etal-2019-recognising
%X We identify agreement and disagreement between utterances that express stances towards a topic of discussion. Existing methods focus mainly on conversational settings, where dialogic features are used for (dis)agreement inference. We extend this scope and seek to detect stance (dis)agreement in a broader setting, where independent stance-bearing utterances, which prevail in many stance corpora and real-world scenarios, are compared. To cope with such non-dialogic utterances, we find that the reasons uttered to back up a specific stance can help predict stance (dis)agreements. We propose a reason comparing network (RCN) to leverage reason information for stance comparison. Empirical results on a well-known stance corpus show that our method can discover useful reason information, enabling it to outperform several baselines in stance (dis)agreement detection.
%R 10.18653/v1/P19-1460
%U https://aclanthology.org/P19-1460
%U https://doi.org/10.18653/v1/P19-1460
%P 4665-4671

Markdown (Informal)

[Recognising Agreement and Disagreement between Stances with Reason Comparing Networks](https://aclanthology.org/P19-1460) (Xu et al., ACL 2019)

• Recognising Agreement and Disagreement between Stances with Reason Comparing Networks (Xu et al., ACL 2019)

ACL

• Chang Xu, Cecile Paris, Surya Nepal, and Ross Sparks. 2019. Recognising Agreement and Disagreement between Stances with Reason Comparing Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4665–4671, Florence, Italy. Association for Computational Linguistics.