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        <title>How effective is incongruity? Implications for code-mixed sarcasm detection</title>
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        <namePart type="given">Aditya</namePart>
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            <title>Proceedings of the 18th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Sivaji</namePart>
            <namePart type="family">Bandyopadhyay</namePart>
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            <namePart type="given">Sobha</namePart>
            <namePart type="given">Lalitha</namePart>
            <namePart type="family">Devi</namePart>
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            <namePart type="given">Pushpak</namePart>
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    <abstract>The presence of sarcasm in conversational systems and social media like chatbots, Facebook, Twitter, etc. poses several challenges for downstream NLP tasks. This is attributed to the fact that the intended meaning of a sarcastic text is contrary to what is expressed. Further, the use of code-mix language to express sarcasm is increasing day by day. Current NLP techniques for code-mix data have limited success due to the use of different lexicon, syntax, and scarcity of labeled corpora. To solve the joint problem of code-mixing and sarcasm detection, we propose the idea of capturing incongruity through sub-word level embeddings learned via fastText. Empirical results show that our proposed model achieves an F1-score on code-mix Hinglish dataset comparable to pretrained multilingual models while training 10x faster and using a lower memory footprint.</abstract>
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%0 Conference Proceedings
%T How effective is incongruity? Implications for code-mixed sarcasm detection
%A Shah, Aditya
%A Maurya, Chandresh
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F shah-maurya-2021-effective
%X The presence of sarcasm in conversational systems and social media like chatbots, Facebook, Twitter, etc. poses several challenges for downstream NLP tasks. This is attributed to the fact that the intended meaning of a sarcastic text is contrary to what is expressed. Further, the use of code-mix language to express sarcasm is increasing day by day. Current NLP techniques for code-mix data have limited success due to the use of different lexicon, syntax, and scarcity of labeled corpora. To solve the joint problem of code-mixing and sarcasm detection, we propose the idea of capturing incongruity through sub-word level embeddings learned via fastText. Empirical results show that our proposed model achieves an F1-score on code-mix Hinglish dataset comparable to pretrained multilingual models while training 10x faster and using a lower memory footprint.
%U https://aclanthology.org/2021.icon-main.32
%P 271-276

Markdown (Informal)

[How effective is incongruity? Implications for code-mixed sarcasm detection](https://aclanthology.org/2021.icon-main.32) (Shah & Maurya, ICON 2021)

• How effective is incongruity? Implications for code-mixed sarcasm detection (Shah & Maurya, ICON 2021)

ACL

• Aditya Shah and Chandresh Maurya. 2021. How effective is incongruity? Implications for code-mixed sarcasm detection. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 271–276, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).