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arXiv · 1705.00440

Data Augmentation for Low-Resource Neural Machine Translation

Marzieh Fadaee·Arianna Bisazza·Christof Monz
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Abstract

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.

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Marzieh Fadaee, Arianna Bisazza, Christof Monz. 2017-05-01. Data Augmentation for Low-Resource Neural Machine Translation. https://doi.org/10.18653/v1%2Fp17-2090

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