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        <title>Simulated multiple reference training improves low-resource machine translation</title>
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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.</abstract>
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%0 Conference Proceedings
%T Simulated multiple reference training improves low-resource machine translation
%A Khayrallah, Huda
%A Thompson, Brian
%A Post, Matt
%A Koehn, Philipp
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F khayrallah-etal-2020-simulated
%X 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.
%R 10.18653/v1/2020.emnlp-main.7
%U https://aclanthology.org/2020.emnlp-main.7
%U https://doi.org/10.18653/v1/2020.emnlp-main.7
%P 82-89

Markdown (Informal)

[Simulated multiple reference training improves low-resource machine translation](https://aclanthology.org/2020.emnlp-main.7) (Khayrallah et al., EMNLP 2020)

• Simulated multiple reference training improves low-resource machine translation (Khayrallah et al., EMNLP 2020)

ACL

• Huda Khayrallah, Brian Thompson, Matt Post, and Philipp Koehn. 2020. Simulated multiple reference training improves low-resource machine translation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 82–89, Online. Association for Computational Linguistics.