IUST_NLP at SemEval-2023 Task 10: Explainable Detecting Sexism with Transformers and Task-adaptive Pretraining
This work addresses the need for explainable detection of online sexism, which is important for moderators and researchers, but it is incremental as it builds on existing transformer methods with task-specific adaptations.
The paper tackled the problem of automatically detecting and classifying sexist content online by proposing transformer-based models with task-adaptive pretraining and ensemble learning, achieving F1-scores of 83%, 64%, and 47% on three subtasks.
This paper describes our system on SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). This work aims to design an automatic system for detecting and classifying sexist content in online spaces. We propose a set of transformer-based pre-trained models with task-adaptive pretraining and ensemble learning. The main contributions of our system include analyzing the performance of different transformer-based pre-trained models and combining these models, as well as providing an efficient method using large amounts of unlabeled data for model adaptive pretraining. We have also explored several other strategies. On the test dataset, our system achieves F1-scores of 83%, 64%, and 47% on subtasks A, B, and C, respectively.