This report describes GMU's sentiment analysis system for the SemEval-2023 shared task AfriSenti-SemEval. We participated in all three sub-tasks: Monolingual, Multilingual, and Zero-Shot. Our approach uses models initialized with AfroXLMR-large, a pre-trained multilingual language model trained on African languages and fine-tuned correspondingly. We also introduce augmented training data along with original training data. Alongside finetuning, we perform phylogeny-based adapter tuning to create several models and ensemble the best models for the final submission. Our system achieves the best F1-score on track 5: Amharic, with 6.2 points higher F1-score than the second-best performing system on this track. Overall, our system ranks 5th among the 10 systems participating in all 15 tracks.
@article{arxiv.2304.12979,
title = {GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters},
author = {Md Mahfuz Ibn Alam and Ruoyu Xie and Fahim Faisal and Antonios Anastasopoulos},
journal= {arXiv preprint arXiv:2304.12979},
year = {2023}
}