UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource languages
This addresses sentiment analysis for African languages, which are often low-resource, but the approach is incremental as it applies existing fine-tuning methods to new data.
The paper tackled sentiment classification for low-resource African languages by fine-tuning a multilingual large language model, finding that monolingual fine-tuning yielded the best results on datasets with thousands of samples.
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.