Team_BUDDI at ComMA@ICON: Exploring Individual and Joint Modelling Approaches for Detecting Aggression, Communal Bias and Gender Bias
Abstract
The ComMA@ICON 2021 Shared Task involved identifying the level of aggression and identifying gender bias and communal bias from texts in various languages from the domain of social media. In this paper, we present the description and analyses of systems we implemented towards these tasks. We built systems utilizing Transformer-based models, experimented by individually and jointly modelling these tasks, and investigated the performance of a feature engineering method in conjunction with a joint modelling approach. We demonstrate that the joint modelling approaches outperform the individual modelling approach in most cases.
Publication
In Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification
