Abstract
This paper describes our system used in the SemEval-2023 Task 10: Towards ExplainableDetection of Online Sexism (Kirk et al., 2023). The harmful effects of sexism on the internet have impacted both men and women, yet current research lacks a fine-grained classification of sexist content. The task involves three hierarchical sub-tasks, which we addressed by employing a multitask-learning framework. To further enhance our system’s performance, we pre-trained the roberta-large (Liu et al., 2019b) and deberta-v3-large (He et al., 2021) models on two million unlabeled data, resulting in significant improvements on sub-tasks A and C. In addition, the multitask-learning approach boosted the performance of our models on subtasks A and B. Our system exhibits promising results in achieving explainable detection of online sexism, attaining a test f1-score of 0.8746 on sub-task A (ranking 1st on the leaderboard), and ranking 5th on sub-tasks B and C.- Anthology ID:
- 2023.semeval-1.304
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2188–2192
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.304
- DOI:
- 10.18653/v1/2023.semeval-1.304
- Cite (ACL):
- Mengyuan Zhou. 2023. PingAnLifeInsurance at SemEval-2023 Task 10: Using Multi-Task Learning to Better Detect Online Sexism. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2188–2192, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- PingAnLifeInsurance at SemEval-2023 Task 10: Using Multi-Task Learning to Better Detect Online Sexism (Zhou, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.semeval-1.304.pdf