  In recent years, transformer models have achieved great success in natural
language processing (NLP) tasks. Most of the current state-of-the-art NLP
results are achieved by using monolingual transformer models, where the model
is pre-trained using a single language unlabelled text corpus. Then, the model
is fine-tuned to the specific downstream task. However, the cost of
pre-training a new transformer model is high for most languages. In this work,
we propose a cost-effective transfer learning method to adopt a strong source
language model, trained from a large monolingual corpus to a low-resource
language. Thus, using XLNet language model, we demonstrate competitive
performance with mBERT and a pre-trained target language model on the
cross-lingual sentiment (CLS) dataset and on a new sentiment analysis dataset
for low-resourced language Tigrinya. With only 10k examples of the given
Tigrinya sentiment analysis dataset, English XLNet has achieved 78.88% F1-Score
outperforming BERT and mBERT by 10% and 7%, respectively. More interestingly,
fine-tuning (English) XLNet model on the CLS dataset has promising results
compared to mBERT and even outperformed mBERT for one dataset of the Japanese
language.
