Many valid translations exist for a given sentence, and yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings. We introduce  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. With an English paraphraser, we demonstrate the effectiveness of our method in low-resource settings , with gains of 1.2 to 7 BLEU .
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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and
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Simulated Multiple Reference Training (SMRT),
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:
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With an English paraphraser, we
We
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our method
SMRT
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, with gains
when translating to English, with improvements
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7 BLEU
7.0 BLEU. We also find SMRT is complementary to back-translation