A Data Augmentation Method for English-Vietnamese Neural Machine Translation

Abstract

The translation quality of machine translation systems depends on the parallel corpus used for training, in particular the quantity and quality of the corpus. However, building a high-quality and large-scale parallel corpus is complex and expensive, particularly for a specific domain parallel corpus. Therefore, data augmentation techniques are widely used in machine translation. The input of the back-translation method is monolingual text, which is available from many sources, and therefore this method can be easily and effectively implemented to generate synthetic parallel data. In practice, monolingual texts can be collected from different sources, in which sources from websites often have errors in grammar and spelling, sentence mismatch or freestyle. Therefore, the quality of the output translation is reduced, leading to a low-quality parallel corpus generated by back-translation. In this study, we propose a method for improving the quality of monolingual texts for back-translation. Moreover, we supplemented the data by pruning translation table. We experimented with an English-Vietnamese neural machine translation using the IWSLT2015 dataset for training and testing in the legal domain. The results showed that the proposed method can effectively augment parallel data for machine translation, thereby improving translation quality. In our experimental cases, the BLEU score is increased by 16.37 points compared to the baseline system.