Simulated Multiple Reference Training Improves Low-Resource Machine Translation

• URL: http://arxiv.org/abs/2004.14524v2
• Date: Tue, 13 Oct 2020 15:43:57 GMT
• Title: Simulated Multiple Reference Training Improves Low-Resource Machine Translation
• Authors: Huda Khayrallah, Brian Thompson, Matt Post, Philipp Koehn
• Abstract summary: We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible translations. We demonstrate the effectiveness of SMRT in low-resource settings when translating to English, with improvements of 1.2 to 7.0 BLEU.
• Score: 22.404646693366054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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