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Learning to capture contrast in sarcasm with contextual dual-view attention network

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Abstract

Sarcasm is a common way of rhetoric in our daily life. It is used to express the opposite of the literal meaning, which makes it a challenging task in sentiment analysis of natural language processing (NLP). The formation mechanism of sarcasm is usually caused by the contrast between the positive sentiment and the negative situation. In this paper, we propose a contextual dual-view attention network (CDVaN) for sarcasm detection according to the formation mechanism of sarcasm. A Contrast Understanding Unit is proposed to effectively extract the contrast between the positive sentiment and the negative situation from the view of formation mechanism of sarcasm. Apart from it, we further use a Context Understanding Unit to extract the contextual semantic information from the contextual semantic view. Our experiments on the IAC-V1 dataset and IAC-V2 dataset demonstrate that the proposed CDVaN model can distinguish sarcasm effectively. The results show that our model achieves state-of-the-art or comparable results.

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Notes

  1. https://www.thefreedictionary.com/.

  2. https://www.sentic.net/downloads/.

  3. https://nlds.soe.ucsc.edu/sarcasm1.

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Acknowledgements

This work is partially supported by grant from the Natural Science Foundation of China (Nos. 62076046, 61632011, 62006034, 61772103), the Fundamental Research Funds for the Central Universities, the Ministry of Education Humanities and Social Science Project (No. 19YJCZH199), the Foundation of State Key Laboratory of Cognitive Intelligence, iFLYTEK, P.R. China (COGOS-20190001, Intelligent Medical Question Answering based on User Profiling and Knowledge Graph).

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  1. Dalian University of Technology, Dalian, China

    Lu Ren, Hongfei Lin, Bo Xu, Liang Yang & Dongyu Zhang

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  1. Lu Ren
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Ren, L., Lin, H., Xu, B. et al. Learning to capture contrast in sarcasm with contextual dual-view attention network. Int. J. Mach. Learn. & Cyber. 12, 2607–2615 (2021). https://doi.org/10.1007/s13042-021-01344-2

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