KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis

Antoine Nzeyimana
4/25/2023
cs.CL

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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.

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Cite this paper

@article{nzeyimana2023kinlp,
  title  = {KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis},
  author = {Antoine Nzeyimana},
  year   = {2023},
  eprint = {2304.12569},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.12569v1}
}

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