홈 › Papers

Aggression and Misogyny Detection using BERT: A Multi-Task Approach

In recent times, the focus of the NLP community has increased towards offensive language, aggression, and hate-speech detection.This paper presents our system for TRAC-2 shared task on {`}Aggression Identification{''} (sub-task A) and {}Misogynistic Aggression Identification{''} (sub-task B). The data for this shared task is provided in three different languages - English, Hindi, and Bengali. Each data instance is annotated into one of the three aggression classes - Not Aggressive, Covertly Aggressive, Overtly Aggressive, as well as one of the two misogyny classes - Gendered and Non-Gendered. We propose an end-to-end neural model using attention on top of BERT that incorporates a multi-task learning paradigm to address both the sub-tasks simultaneously. Our team, {`}na14{''}, scored 0.8579 weighted F1-measure on the English sub-task B and secured 3rd rank out of 15 teams for the task. The code and the model weights are publicly available at https://github.com/NiloofarSafi/TRAC-2. Keywords: Aggression, Misogyny, Abusive Language, Hate-Speech Detection, BERT, NLP, Neural Networks, Social Media

Code (1)

Tasks

Methods 이 논문이 사용한 방법론

Similar Papers 제목 키워드 기반