KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis
This work addresses sentiment analysis for the under-resourced African language Kinyarwanda, representing an incremental improvement in a domain-specific task.
The paper tackled sentiment analysis for Kinyarwanda tweets by developing a language-specific model with a two-tier transformer architecture and multi-task pre-training, achieving second place out of 34 teams with a 72.50% weighted F1 score.
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.