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
[email protected] 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.