Fine-tuning Pre-Trained Transformer based model for Hate Speech and Offensive Content Identification in English, Indo-Aryan and Code-Mixed (English-Hindi) languages

Abstract

Hate Speech and Offensive Content Identification is one of the most challenging problem in the natural language processing field, being imposed by the rising presence of this phenomenon in online social media. This paper describes our Transformer-based solutions for identifying offensive language on Twitter in three languages (i.e., English, Hindi, and Marathi) and one code mixed (English-Hindi) language, which was employed in Subtask 1A, Subtask 1B and Subtask 2 of the HASOC 2021 shared task. Finally, the highest-scoring models were used for our submissions in the competition, which ranked our IRLab@IITBHU team 16th of 56, 18th of 37, 13th of 34, 7th of 24, 12th of 25 and 6th of 16 for English Subtask 1A, English Subtask 1B, Hindi Subtask 1A, Hindi Subtask 1B, Marathi Subtask 1A, and English-Hindi Code-Mix Subtask 2 respectively. © 2021 Copyright for this paper by its authors.