Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction
Changrong Min, Ximing Li, Liang Yang, Zhilin Wang, Bo Xu, Hongfei Lin
Abstract
Sarcasm, as a form of irony conveying mockery and contempt, has been widespread in social media such as Twitter and Weibo, where the sarcastic text is commonly characterized as an incongruity between the surface positive and negative situation. Naturally, it has an urgent demand to automatically identify sarcasm from social media, so as to illustrate people’s real views toward specific targets. In this paper, we develop a novel sarcasm detection method, namely Sarcasm Detector with Augmentation of Potential Result and Reaction (SD-APRR). Inspired by the direct access view, we treat each sarcastic text as an incomplete version without latent content associated with implied negative situations, including the result and human reaction caused by its observable content. To fill the latent content, we estimate the potential result and human reaction for each given training sample by [xEffect] and [xReact] relations inferred by the pre-trained commonsense reasoning tool COMET, and integrate the sample with them as an augmented one. We can then employ those augmented samples to train the sarcasm detector, whose encoder is a graph neural network with a denoising module. We conduct extensive empirical experiments to evaluate the effectiveness of SD-APRR. The results demonstrate that SD-APRR can outperform strong baselines on benchmark datasets.- Anthology ID:
- 2023.acl-long.566
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10172–10183
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.566
- DOI:
- 10.18653/v1/2023.acl-long.566
- Cite (ACL):
- Changrong Min, Ximing Li, Liang Yang, Zhilin Wang, Bo Xu, and Hongfei Lin. 2023. Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10172–10183, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction (Min et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.acl-long.566.pdf