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        <title>UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource Languages</title>
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            <namePart type="given">Atul</namePart>
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            <namePart type="given">Harish</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource Languages
%A Rønningstad, Egil
%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 ronningstad-2023-uio
%X 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.
%R 10.18653/v1/2023.semeval-1.144
%U https://aclanthology.org/2023.semeval-1.144
%U https://doi.org/10.18653/v1/2023.semeval-1.144
%P 1054-1060

Markdown (Informal)

[UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource Languages](https://aclanthology.org/2023.semeval-1.144) (Rønningstad, SemEval 2023)

• UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource Languages (Rønningstad, SemEval 2023)

ACL

• Egil Rønningstad. 2023. UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource Languages. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1054–1060, Toronto, Canada. Association for Computational Linguistics.