LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for
  Sexism Detection and Classification
Konstantin Chernyshev Ekaterina Garanina Duygu Bayram Qiankun Zheng Lukas Edman
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.