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Multi-perspective contrastive learning framework guided by sememe knowledge and label information for sarcasm detection

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Abstract

Sarcasm is a prevailing rhetorical device that intentionally uses words that literally meaning opposite the real meaning. Due to this deliberate ambiguity, accurately detecting sarcasm can encourage the comprehension of users’ real intentions. Therefore, sarcasm detection is a critical and challenging task for sentiment analysis. In previous research, neural network-based models are generally unsatisfactory when dealing with complex sarcastic expressions. To ameliorate this situation, we propose a multi-perspective contrastive learning framework for sarcasm detection, called SLGC, which is guided by sememe knowledge and label information based on the pre-trained neural model. For the in-instance perspective, we leverage the sememe, the minimum meaning unit, to guide the contrastive learning to produce high-quality sentence representations. For the between-instance perspective, we utilize label information to guide contrastive learning to mine potential interaction relationships between sarcastic expressions. Experiments on two public benchmark sarcasm detection dataset demonstrate that our approach significantly outperforms the current state-of-the-art model.

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Data availability

The partition version of the GuanSarcasm dataset generated during the current study is available from the corresponding author upon reasonable request.

Notes

  1. https://www.merriam-webster.com/.

  2. https://en.wikipedia.org/wiki/Sememe.

  3. https://github.com/thunlp/OpenHowNet.

  4. https://www.guancha.cn/.

  5. https://nlds.soe.ucsc.edu/sarcasm2.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (62006062, 62176076), Natural Science Foundation of GuangDong 2023A1515012922, the Shenzhen Foundational Research Funding (JCYJ20220818102415032), the Major Key Project of PCL2021A06, Guangdong Provincial Key Labo-ratory of Novel Security Intelligence Technologies 2022B1212010005.

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Authors and Affiliations

  1. Joint Lab of HITSZ-CMS, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, Guangdong, China

    Zhiyuan Wen, Rui Wang, Xuan Luo, Qianlong Wang, Bin Liang, Jiachen Du & Ruifeng Xu

  2. King′s College London, London, UK

    Lin Gui

  3. Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Shenzhen, 518000, Guangdong, China

    Zhiyuan Wen, Rui Wang, Xuan Luo, Qianlong Wang, Bin Liang, Jiachen Du & Ruifeng Xu

  4. Peng Cheng Lab, Shenzhen, 518000, Guangdong, China

    Ruifeng Xu

  5. China Merchants Securities Co., Ltd, Shenzhen, 518000, Guangdong, China

    Xiaoqi Yu

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Wen, Z., Wang, R., Luo, X. et al. Multi-perspective contrastive learning framework guided by sememe knowledge and label information for sarcasm detection. Int. J. Mach. Learn. & Cyber. 14, 4119–4134 (2023). https://doi.org/10.1007/s13042-023-01884-9

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