Code-Switching Patterns Can Be an Effective Route to Improve Performance of Downstream NLP Applications: A Case Study of Humour, Sarcasm and Hate Speech Detection

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1018–1023 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 1018 Code-Switching Patterns Can Be an Effective Route to Improve Performance of Downstream NLP Applications: A Case Study of Humour, Sarcasm and Hate Speech Detection Srijan Bansal1, G Vishal2, Ayush Suhane3, Jasabanta Patro4, Animesh Mukherjee5 Indian Institute of Technology, Kharagpur, West Bengal, India - 721302 {1srijanbansal97, 2vishal g, 3ayushsuhane99 }@iitkgp.ac.in, 4jasabantapatro@iitkgp.ac.in, 5animeshm@cse.iitkgp.ac.in Abstract In this paper we demonstrate how code- switching patterns can be utilised to improve various downstream NLP applications. In par- ticular, we encode different switching features to improve humour, sarcasm and hate speech detection tasks. We believe that this simple linguistic observation can also be potentially helpful in improving other similar NLP appli- cations. 1 Introduction Code-mixing/switching in social media has become commonplace. Over the past few years, the NLP research community has in fact started to vigor- ously investigate various properties of such code- switched posts to build downstream applications. The author in (Hidayat, 2012) demonstrated that inter-sentential switching is preferred more than intra-sentential switching by Facebook users. Fur- ther while 45% of the switching was done for real lexical needs, 40% was for discussing a particular topic and 5% for content classification. In another study (Dey and Fung, 2014) interviewed Hindi- English bilingual students and reported that 67% of the words were in Hindi and 33% in English. Re- cently, many down stream applications have been designed for code-mixed text. (Han et al., 2012) attempted to construct a normalisation dictionary offline using the distributional similarity of tokens plus their string edit distance. (Vyas et al., 2014) developed a POS tagging framework for Hindi- English data. More nuanced applications like humour detec- tion (Khandelwal et al., 2018), sarcasm detec- tion (Swami et al., 2018) and hate speech detec- tion (Bohra et al., 2018) have been targeted for code-switched data in the last two to three years. 1.1 Motivation The primary motivation for the current work is de- rived from (Vizca´ıno, 2011) where the author notes – “The switch itself may be the object of humour”. In fact, (Siegel, 1995) has studied humour in the Fijian language and notes that when trying to be comical, or convey humour, speakers switch from Fijian to Hindi. Therefore, humour here is pro- duced by the change of code rather than by the referential meaning or content of the message. The paper also talks about similar phenomena observed in Spanish-English cases. In a study of English-Hindi code-switching and swearing patterns on social networks (Agarwal et al., 2017), the authors show that when people code-switch, there is a strong preference for swear- ing in the dominant language. These studies to- gether lead us to hypothesize that the patterns of switching might be useful in building various NLP applications. 1.2 The present work To corroborate our hypothesis, in this paper, we consider three downstream applications – (i) hu- mour detection (Khandelwal et al., 2018), (ii) sar- casm detection (Swami …

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