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
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.emnlp-main.7.pdf
- Data
- ParaBank