AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages vs MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

AfriSenti is a sentiment analysis benchmark of more than 110,000 tweets in 14 African languages spanning four language families, annotated by native speakers. It underpinned SemEval-2023 Task 12, a shared task that attracted more than 200 participants, and documents data collection, annotation and baseline methods for low-resource languages.

MasakhaNER 2.0 introduces the largest human-annotated named entity recognition dataset for 20 African languages and studies Africa-centric cross-lingual transfer learning. The paper reports that choosing the best transfer language improves zero-shot F1 by an average of 14 points across the 20 languages compared with transferring from English.