Recognising agreement and disagreement between stances with reason comparing networks

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Xu, C; Paris, C; Nepal, S; Sparks, RDate
2020-01-01Source Title
ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the ConferencePublisher
The Association for Computational LinguisticsUniversity of Melbourne Author/s
Xu, ChangAffiliation
Computing and Information SystemsMetadata
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Conference PaperCitations
Xu, C., Paris, C., Nepal, S. & Sparks, R. (2020). Recognising agreement and disagreement between stances with reason comparing networks. ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, pp.4665-4671. The Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1460.Access Status
Open AccessAbstract
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.
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