LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for Sexism Detection and Classification
This work addresses the need for more interpretable systems in online sexism detection, but it is incremental as it applies existing methods to a specific competition task.
The paper tackled the problem of detecting and classifying online sexism by comparing domain-adaptive pre-training with fine-tuning and multi-task learning, finding that multi-task learning performed on par for detection and better for coarse-grained classification, while fine-tuning was preferable for fine-grained classification.
Misogyny and sexism are growing problems in social media. Advances have been made in online sexism detection but the systems are often uninterpretable. SemEval-2023 Task 10 on Explainable Detection of Online Sexism aims at increasing explainability of the sexism detection, and our team participated in all the proposed subtasks. Our system is based on further domain-adaptive pre-training (Gururangan et al., 2020). Building on the Transformer-based models with the domain adaptation, we compare fine-tuning with multi-task learning and show that each subtask requires a different system configuration. In our experiments, multi-task learning performs on par with standard fine-tuning for sexism detection and noticeably better for coarse-grained sexism classification, while fine-tuning is preferable for fine-grained classification.