Abstract
In our work, a model is implemented that solves the task, based on multilingual pre-trained models. We also consider various methods of data preprocessing- Anthology ID:
- 2023.semeval-1.212
- 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:
- 1537–1541
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.212
- DOI:
- 10.18653/v1/2023.semeval-1.212
- Cite (ACL):
- Daniil Homskiy and Narek Maloyan. 2023. DN at SemEval-2023 Task 12: Low-Resource Language Text Classification via Multilingual Pretrained Language Model Fine-tuning. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1537–1541, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- DN at SemEval-2023 Task 12: Low-Resource Language Text Classification via Multilingual Pretrained Language Model Fine-tuning (Homskiy & Maloyan, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2023.semeval-1.212.pdf