Findings of the Shared Task on Offensive Language Identification in Tamil, Malayalam, and Kannada
Findings of the Shared Task on Offensive Language Identification in Tamil, Malayalam, and Kannada Bharathi Raja Chakravarthi1, Ruba Priyadharshini2, Navya Jose3 Anand Kumar M 4,Thomas Mandl 5, Prasanna Kumar Kumaresan3, Rahul Ponnusamy3, Hariharan R L 4, John Philip McCrae 1 and Elizabeth Sherly3 1National University of Ireland Galway, 2Madurai Kamaraj University, 3Indian Institute of Information Technology and Management-Kerala, 4National Institute of Technology Karnataka Surathkal, 5University of Hildesheim Germany bharathi.raja@insight-centre.org Abstract Research in hate speech detection (Kumar et al., Detecting offensive language in social me- 2018) or offensive language detection (Zampieri dia in local languages is critical for mod- et al., 2020; Mandl et al., 2020) using Natural Lan- erating user-generated content. Thus, the guage Processing (NLP) has significantly improved field of offensive language identification for in recent years. However, the work on under- under-resourced languages like Tamil, Malay- resourced languages is still limited (Chakravarthi, alam and Kannada is of essential impor- 2020). For example, under-resourced languages tance. As user-generated content is often such as Tamil, Malayalam, and Kannada lack tools code-mixed and not well studied for under- resourced languages, it is imperative to cre- and datasets (Chakravarthi et al., 2020a,c; Thava- ate resources and conduct benchmark stud- reesan and Mahesan, 2019, 2020a,b). Recently, ies to encourage research in under-resourced shared sentiment analysis for Tamil and Malay- Dravidian languages. We created a shared alam by Chakravarthi et al.(2020d) and offensive task on offensive language detection in Dra- language identification in Tamil and Malayalam vidian languages. We summarize the dataset by Chakravarthi et al.(2020b) paved the wave for this challenge which are openly avail- for more research on Dravidian languages.
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