Reasoning with Sarcasm by Reading In-Between

Yi Tay, Anh Tuan Luu, Siu Cheung Hui, Jian Su


Abstract
Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit. The prevalence of sarcasm on the social web is highly disruptive to opinion mining systems due to not only its tendency of polarity flipping but also usage of figurative language. Sarcasm commonly manifests with a contrastive theme either between positive-negative sentiments or between literal-figurative scenarios. In this paper, we revisit the notion of modeling contrast in order to reason with sarcasm. More specifically, we propose an attention-based neural model that looks in-between instead of across, enabling it to explicitly model contrast and incongruity. We conduct extensive experiments on six benchmark datasets from Twitter, Reddit and the Internet Argument Corpus. Our proposed model not only achieves state-of-the-art performance on all datasets but also enjoys improved interpretability.
Anthology ID:
P18-1093
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1010–1020
Language:
URL:
https://aclanthology.org/P18-1093
DOI:
10.18653/v1/P18-1093
Bibkey:
Cite (ACL):
Yi Tay, Anh Tuan Luu, Siu Cheung Hui, and Jian Su. 2018. Reasoning with Sarcasm by Reading In-Between. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1010–1020, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Reasoning with Sarcasm by Reading In-Between (Tay et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1093.pdf
Presentation:
 P18-1093.Presentation.pdf
Video:
 https://vimeo.com/285802146