Copied Monolingual Data Improves Low-Resource Neural Machine Translation

Abstract

We train a neural machine translation (NMT) system to both translate source-language text and copy target-language text, thereby exploiting monolingual corpora in the target language. Specifically, we create a bitext from the monolingual text in the target language so that each source sentence is identical to the target sentence. This copied data is then mixed with the parallel corpus and the NMT system is trained like normal, with no metadata to distinguish the two input languages. Our proposed method proves to be an effective way of incorporating monolingual data into low-resource NMT. see gains of up to 1.2 BLEU over a strong baseline with back-translation. Further analysis shows that the linguis-tic phenomena behind these gains are different from and largely orthogonal to back-translation, with our copied corpus method improving accuracy on named entities and other words that should remain identical between the source and target languages.