GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters
Md Mahfuz Ibn Alam, Ruoyu Xie, Fahim Faisal, Antonios Anastasopoulos
The 17th International Workshop on Semantic Evaluation (SemEval-2023) Task 12: afrisenti-semeval: sentiment analysis for low-resource african languages using twitter dataset Paper
TLDR:
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 A
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Abstract:
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.