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arXiv · 2004.14524

Simulated Multiple Reference Training Improves Low-Resource Machine Translation

Huda Khayrallah·Brian Thompson·Matt Post·Philipp Koehn
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Abstract

Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings. We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible translations by sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser's distribution over possible tokens. We demonstrate the effectiveness of SMRT in low-resource settings when translating to English, with improvements of 1.2 to 7.0 BLEU. We also find SMRT is complementary to back-translation.

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Huda Khayrallah, Brian Thompson, Matt Post, Philipp Koehn. 2020-04-30. Simulated Multiple Reference Training Improves Low-Resource Machine Translation. https://doi.org/10.18653/v1/2020.emnlp-main.7

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