Abstract
This paper describes the system entered by the author to the SemEval-2023 Task 12: Sentiment analysis for African languages. The system focuses on the Kinyarwanda language and uses a language-specific model. Kinyarwanda morphology is modeled in a two tier transformer architecture and the transformer model is pre-trained on a large text corpus using multi-task masked morphology prediction. The model is deployed on an experimental platform that allows users to experiment with the pre-trained language model fine-tuning without the need to write machine learning code. Our final submission to the shared task achieves second ranking out of 34 teams in the competition, achieving 72.50% weighted F1 score. Our analysis of the evaluation results highlights challenges in achieving high accuracy on the task and identifies areas for improvement.- Anthology ID:
- 2023.semeval-1.98
- 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:
- 718–723
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.98
- DOI:
- 10.18653/v1/2023.semeval-1.98
- Cite (ACL):
- Antoine Nzeyimana. 2023. KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 718–723, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis (Nzeyimana, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.semeval-1.98.pdf