AU_NLP at SemEval-2023 Task 10: Explainable Detection of Online Sexism Using Fine-tuned RoBERTa

Amit Das, Nilanjana Raychawdhary, Tathagata Bhattacharya, Gerry Dozier, Cheryl D. Seals


Abstract
Social media is a concept developed to link people and make the globe smaller. But it has recently developed into a center for sexist memes that target especially women. As a result, there are more events of hostile actions and harassing remarks present online. In this paper, we introduce our system for the task of online sexism detection, a part of SemEval 2023 task 10. We introduce fine-tuned RoBERTa model to address this specific problem. The efficiency of the proposed strategy is demonstrated by the experimental results reported in this research.
Anthology ID:
2023.semeval-1.97
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:
707–717
Language:
URL:
https://aclanthology.org/2023.semeval-1.97
DOI:
10.18653/v1/2023.semeval-1.97
Bibkey:
Cite (ACL):
Amit Das, Nilanjana Raychawdhary, Tathagata Bhattacharya, Gerry Dozier, and Cheryl D. Seals. 2023. AU_NLP at SemEval-2023 Task 10: Explainable Detection of Online Sexism Using Fine-tuned RoBERTa. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 707–717, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
AU_NLP at SemEval-2023 Task 10: Explainable Detection of Online Sexism Using Fine-tuned RoBERTa (Das et al., SemEval 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.semeval-1.97.pdf