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Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Multilingual transformer models like mBERT and XLM-RoBERTa have obtained great improvements for many NLP tasks on a variety of languages. However, recent works also showed that results from high-resource languages could not be easily transferred to realistic, low-resource scenarios. In this work, we study trends in performance for different amounts of available resources for the three African languages Hausa, isiXhosa and Yorùbá on both NER and topic classification. We show that in combinationdoi:10.18653/v1/2020.emnlp-main.204 fatcat:3m6s3ylpw5avrkyeieg4puirce