Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages
Abstract
AfriSenti-SemEval Shared Task 12 of SemEval-2023. The task aims to perform monolingual sentiment classification (sub-task A) for 12 African languages, multilingual sentiment classification (sub-task B), and zero-shot sentiment classification (task C). For sub-task A, we conducted experiments using classical machine learning classifiers, Afro-centric language models, and language-specific models. For task B, we fine-tuned multilingual pre-trained language models that support many of the languages in the task. For task C, we used we make use of a parameter-efficient Adapter approach that leverages monolingual texts in the target language for effective zero-shot transfer. Our findings suggest that using pre-trained Afro-centric language models improves performance for low-resource African languages. We also ran experiments using adapters for zero-shot tasks, and the results suggest that we can obtain promising results by using adapters with a limited amount of resources.
Keywords
Cite
@article{arxiv.2304.06459,
title = {Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages},
author = {Israel Abebe Azime and Sana Sabah Al-Azzawi and Atnafu Lambebo Tonja and Iyanuoluwa Shode and Jesujoba Alabi and Ayodele Awokoya and Mardiyyah Oduwole and Tosin Adewumi and Samuel Fanijo and Oyinkansola Awosan and Oreen Yousuf},
journal= {arXiv preprint arXiv:2304.06459},
year = {2023}
}
Comments
SemEval 2023