Conference Papers
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Item Findings of the Shared Task on Offensive Language Identification in Tamil, Malayalam, and Kannada(Association for Computational Linguistics (ACL), 2021) Chakravarthi, B.R.; Priyadharshini, R.; Jose, N.; Anand Kumar, M.; Mandl, T.; Kumaresan, P.K.; Ponnusamy, R.; LekshmiAmmal, R.L.; Mccrae, J.P.; Sherly, E.Detecting offensive language in social media in local languages is critical for moderating user-generated content. Thus, the field of offensive language identification for under-resourced languages like Tamil, Malayalam and Kannada is of essential importance. As user-generated content is often code-mixed and not well studied for under-resourced languages, it is imperative to create resources and conduct benchmark studies to encourage research in under-resourced Dravidian languages. We created a shared task on offensive language detection in Dravidian languages. We summarize the dataset for this challenge which are openly available at https://competitions.codalab.org/competitions/27654, and present an overview of the methods and the results of the competing systems. ©2021 Association for Computational LinguisticsItem Overview of the HASOC-DravidianCodeMix Shared Task on Offensive Language Detection in Tamil and Malayalam(CEUR-WS, 2021) Chakravarthi, B.R.; Kumaresan, P.K.; Sakuntharaj, R.; Anand Kumar, M.; Thavareesan, S.; Premjith, B.; Sreelakshmi, K.; Subalalitha, S.C.; Mccrae, J.P.; Mandl, T.We present the results of HASOC-Dravidian-CodeMix shared task1 held at FIRE 2021, a track on offensive language identification for Dravidian languages in Code-Mixed Text in this paper. This paper will detail the task, its organisation, and the submitted systems. The identification of offensive language was viewed as a classification task. For this, 16 teams participated in identifying offensive language from Tamil-English code mixed data, 11 teams for Malayalam-English code mixed data and 14 teams for Tamil data. The teams detected offensive language using various machine learning and deep learning classification models. This paper has analysed those benchmark systems to find out how well they accommodate a code-mixed scenario in Dravidian languages, focusing on Tamil and Malayalam. © 2021 Copyright for this paper by its authors.Item Findings of Shared Task on Offensive Language Identification in Tamil and Malayalam(Association for Computing Machinery, 2021) Kumaresan, P.K.; Premjith; Sakuntharaj, R.; Thavareesan, S.; Subalalitha, S.; Anand Kumar, M.; Chakravarthi, B.R.; Mccrae, J.P.We present the results of HASOC-Dravidian-CodeMix shared task1 held at FIRE 2021, a track on offensive language identification for Dravidian languages in Code-Mixed Text in this paper. This paper will detail the task, its organisation, and the submitted systems. The identification of offensive language was viewed as a classification task. For this, 16 teams participated in identifying offensive language from Tamil-English code mixed data, 11 teams for Malayalam-English code mixed data and 14 teams for Tamil data. The teams detected offensive language using various machine learning and deep learning classification models. This paper has analysed those benchmark systems to find out how well they accommodate a code-mixed scenario in Dravidian languages, focusing on Tamil and Malayalam. © 2021 Owner/Author.