Low-Resource Neural Machine Translation Improvement Using Data Augmentation Strategies

Abstract

The development of neural models has greatly improved the performance of machine translation, but these methods require large-scale parallel data, which can be difficult to obtain for low-resource language pairs. To address this issue, this research employs a pre-trained multilingual model and fine-tunes it by using a small bilingual dataset. Additionally, two data-augmentation strategies are proposed to generate new training data: (i) back-translation with the dataset from the source language; (ii) data augmentation via the English pivot language. The proposed approach is applied to the Khmer-Vietnamese machine translation. Experimental results show that our proposed approach outperforms the Google Translator model by 5.3% in terms of BLEU score on a test set of 2,000 Khmer-Vietnamese sentence pairs.