Abstract
Our contribution to the 2023 AfriSenti-SemEval shared task 12: Sentiment Analysis for African Languages, provides insight into how a multilingual large language model can be a resource for sentiment analysis in languages not seen during pretraining. The shared task provides datasets of a variety of African languages from different language families. The languages are to various degrees related to languages used during pretraining, and the language data contain various degrees of code-switching. We experiment with both monolingual and multilingual datasets for the final fine-tuning, and find that with the provided datasets that contain samples in the thousands, monolingual fine-tuning yields the best results.- Anthology ID:
- 2023.semeval-1.144
- Volume:
- Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1054–1060
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.144
- DOI:
- Cite (ACL):
- Egil Rønningstad. 2023. UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource Languages. In Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1054–1060, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource Languages (Rønningstad, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2023.semeval-1.144.pdf