Papers › Aggression and Misogyny Detection using BERT: A Multi-Task Approach
Aggression and Misogyny Detection using BERT: A Multi-Task Approach
Niloofar Safi Samghabadi, Parth Patwa, Srinivas Pykl, Prerana Mukherjee, Amitava Das, Thamar Solorio
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
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