Exploring Low-Resource Machine Translation: Case Study of Lao-Vietnamese Translation

Despite ongoing research efforts in recent years, machine translation in low-resource settings remains challenging. This paper explores recent methods to enhance low-resource machine translation (LoResMT), especially for both-side low-resource language pairs like the Lao-Vietnamese language pair. In this case, our experiments highlight the importance of translation network’s hyper-parameter optimization for LoResMT. Back-translation methods and fine-tuning multilingual pre-trained models also gain positive results. With the small size of the Lao-Vietnamese dataset from the VLSP 2023 MT challenge shared task, our hyper-parameter optimization achieved 24.61 BLEU score, an improvement of 22 BLEU score over the default model settings. Using back-translation method boosts the performance to 27.79 BLEU score. Additionally, fine-tuning the pre-trained model mT5 on the training set yields a result of 28.05 BLEU score. The best achieved BLEU score of our Lao-Vietnamese translation system is comparable to the best score announced in the related works with less data.