Attention at SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS)
This work addresses the need for explainable detection of online sexism, which is an incremental improvement in a domain-specific competition setting.
The paper tackled the problem of detecting and categorizing online sexism through classification tasks, achieving macro F1 scores of 0.839 for binary detection, 0.6228 for category classification, and 0.3693 for fine-grained categorization.
In this paper, we have worked on interpretability, trust, and understanding of the decisions made by models in the form of classification tasks. The task is divided into 3 subtasks. The first task consists of determining Binary Sexism Detection. The second task describes the Category of Sexism. The third task describes a more Fine-grained Category of Sexism. Our work explores solving these tasks as a classification problem by fine-tuning transformer-based architecture. We have performed several experiments with our architecture, including combining multiple transformers, using domain adaptive pretraining on the unlabelled dataset provided by Reddit and Gab, Joint learning, and taking different layers of transformers as input to a classification head. Our system (with team name Attention) was able to achieve a macro F1 score of 0.839 for task A, 0.5835 macro F1 score for task B and 0.3356 macro F1 score for task C at the Codalab SemEval Competition. Later we improved the accuracy of Task B to 0.6228 and Task C to 0.3693 in the test set.