AfriBERTa
Related Papers
Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning
AbstractMultilingual pre-trained language models (PLMs) have demonstrated impressive performance on several downstream tasks for both high-resourced and low-resourced languages. However, there is still a large performance drop for languages unseen …...
Howard University Computer Science at SemEval-2023 Task 12: A 2-Step System Design for Multilingual Sentiment Classification with Language Identification
AbstractThe recent release of the AfriSenti-SemEval shared Task 12 has made available 14 new datasets annotated for sentiment analysis on African Languages. We proposed and evaluated two approaches to this …...
Mini But Mighty: Efficient Multilingual Pretraining with Linguistically-Informed Data Selection
AbstractWith the prominence of large pretrained language models, low-resource languages are rarely modelled monolingually and become victims of the “curse of multilinguality” in massively multilingual models. Recently, AfriBERTa showed that …...
Seals_Lab at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages, Hausa and Igbo
AbstractOne of the most extensively researched applications in natural language processing (NLP) is sentiment analysis. While the majority of the study focuses on high-resource languages (e.g., English), this research will …...
Witcherses at SemEval-2023 Task 12: Ensemble Learning for African Sentiment Analysis
AbstractThis paper describes our system submission for SemEval-2023 Task 12 AfriSenti-SemEval: Sentiment Analysis for African Languages. We propose an XGBoost-based ensemble model trained on emoticon frequency-based features and the predictions …...