Litcius/Paper detail

IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian Languages

Konthala Yasaswini, Karthik Puranik, Adeep Hande, Ruba Priyadharshini, Sajeetha Thavareesan, Bharathi Raja Chakravarthi

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Abstract

This paper demonstrates our work for the shared task on Offensive Language Identification in Dravidian Languages-EACL 2021. Offensive language detection in the various social media platforms was identified previously. But with the increase in diversity of users, there is a need to identify the offensive language in multilingual posts that are largely code-mixed or written in a non-native script. We approach this challenge with various transfer learning-based models to classify a given post or comment in Dravidian languages (Malayalam, Tamil, and Kannada) into 6 categories. The source codes for our systems are published.

Topics & Concepts

OffensiveTamilComputer scienceLanguage identificationMalayalamNatural language processingIdentification (biology)Artificial intelligenceLinguisticsTask (project management)Natural languageEngineeringPhilosophyBiologyOperations researchBotanySystems engineeringHate Speech and Cyberbullying DetectionSwearing, Euphemism, Multilingualism