Abstract
Identifying hate speech is a challenging specialization in the natural language processing field (NLP). Particularly in fields with differing linguistics, it becomes more demanding to construct a well-performing classifier for the betterment of the community. In this paper, we leveraged the performances of pre-trained models on the given hate speech detection dataset. By conducting a hyperparameter search, we computed the feasible setups for fine-tuning and trained effective classifiers that performed well in both subtasks in the HSD-2Lang 2024 contest.- Anthology ID:
- 2024.case-1.27
- Volume:
- Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julians, Malta
- Editors:
- Ali Hürriyetoğlu, Hristo Tanev, Surendrabikram Thapa, Gökçe Uludoğan
- Venues:
- CASE | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 195–198
- Language:
- URL:
- https://aclanthology.org/2024.case-1.27
- DOI:
- Cite (ACL):
- Utku Yagci, Egemen Iscan, and Ahmet Kolcak. 2024. ReBERT at HSD-2Lang 2024: Fine-Tuning BERT with AdamW for Hate Speech Detection in Arabic and Turkish. In Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024), pages 195–198, St. Julians, Malta. Association for Computational Linguistics.
- Cite (Informal):
- ReBERT at HSD-2Lang 2024: Fine-Tuning BERT with AdamW for Hate Speech Detection in Arabic and Turkish (Yagci et al., CASE-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.case-1.27.pdf