Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages
This addresses the problem of limited labeled data for NLP in low-resource African languages, but it is incremental as it builds on existing multilingual models and techniques.
The study tackled the challenge of transferring multilingual transformer models to low-resource African languages like Hausa, isiXhosa, and Yorùbá, showing that with transfer learning or distant supervision, models achieved comparable performance to baselines using only 10 or 100 labeled sentences for NER and topic classification tasks, though some settings did not yield improvements.
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 combination with transfer learning or distant supervision, these models can achieve with as little as 10 or 100 labeled sentences the same performance as baselines with much more supervised training data. However, we also find settings where this does not hold. Our discussions and additional experiments on assumptions such as time and hardware restrictions highlight challenges and opportunities in low-resource learning.