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        <title>KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis</title>
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            <namePart type="given">Atul</namePart>
            <namePart type="given">Kr.</namePart>
            <namePart type="family">Ojha</namePart>
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            <namePart type="given">Seza</namePart>
            <namePart type="family">Doğruöz</namePart>
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            <namePart type="given">Giovanni</namePart>
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            <namePart type="given">Harish</namePart>
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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.</abstract>
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%0 Conference Proceedings
%T KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis
%A Nzeyimana, Antoine
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F nzeyimana-2023-kinlp
%X 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.
%R 10.18653/v1/2023.semeval-1.98
%U https://aclanthology.org/2023.semeval-1.98
%U https://doi.org/10.18653/v1/2023.semeval-1.98
%P 718-723

Markdown (Informal)

[KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis](https://aclanthology.org/2023.semeval-1.98) (Nzeyimana, SemEval 2023)

• KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis (Nzeyimana, SemEval 2023)

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.