The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora. For low-resource language pairs this is not the case, resulting in poor translation quality. Inspired by work in computer vision, we propose a novel data augmentation approach that targets low-frequency words by generating new sentence pairs containing rare words in new, synthetically created contexts. Experimental results on simulated low-resource settings show that our method improves translation quality by up to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.
REF propose an effective data augmentation method that generates sentence pairs containing rare words in synthetically created contexts, but this requires parallel training data not available in the fully unsupervised adaptation setting.
Data Augmentation for Low-Resource Neural Machine Translation
ArXiv
ArXiv
Computer Science