niceNLP at SemEval-2023 Task 10: Dual Model Alternate Pseudo-labeling Improves Your Predictions

Yu Chang, Yuxi Chen, Yanru Zhang


Abstract
Sexism is a growing online problem. It harms women who are targeted and makes online spaces inaccessible and unwelcoming. In this paper, we present our approach for Task A of SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS), which aims to perform binary sexism detection on textual content. To solve this task, we fine-tune the pre-trained model based on several popular natural language processing methods to improve the generalization ability in the face of different data. According to the experimental results, the effective combination of multiple methods enables our approach to achieve excellent performance gains.
Anthology ID:
2023.semeval-1.41
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:
307–311
Language:
URL:
https://aclanthology.org/2023.semeval-1.41
DOI:
10.18653/v1/2023.semeval-1.41
Bibkey:
Cite (ACL):
Yu Chang, Yuxi Chen, and Yanru Zhang. 2023. niceNLP at SemEval-2023 Task 10: Dual Model Alternate Pseudo-labeling Improves Your Predictions. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 307–311, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
niceNLP at SemEval-2023 Task 10: Dual Model Alternate Pseudo-labeling Improves Your Predictions (Chang et al., SemEval 2023)
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PDF:
https://aclanthology.org/2023.semeval-1.41.pdf