Simulated multiple reference training improves low-resource machine translation

Huda Khayrallah, Brian Thompson, Matt Post, Philipp Koehn


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.
Anthology ID:
2020.emnlp-main.7
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
82–89
Language:
URL:
https://aclanthology.org/2020.emnlp-main.7
DOI:
10.18653/v1/2020.emnlp-main.7
Bibkey:
Cite (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.
Cite (Informal):
Simulated multiple reference training improves low-resource machine translation (Khayrallah et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.7.pdf
Video:
 https://slideslive.com/38938786
Data
ParaBank