Recognising agreement and disagreement between stances with reason comparing networks
AuthorXu, C; Paris, C; Nepal, S; Sparks, R
Source TitleACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherThe Association for Computational Linguistics
University of Melbourne Author/sXu, Chang
AffiliationComputing and Information Systems
Document TypeConference Paper
CitationsXu, 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 StatusOpen Access
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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