Data Augmentation for Low-Resource Neural Machine Translation
Marzieh Fadaee, Arianna Bisazza, Christof Monz · 2017
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.