Simulated Multiple Reference Training Improves Low-Resource Machine Translation
Computation and Language
2021-04-23 v2
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.
Cite
@article{arxiv.2004.14524,
title = {Simulated Multiple Reference Training Improves Low-Resource Machine Translation},
author = {Huda Khayrallah and Brian Thompson and Matt Post and Philipp Koehn},
journal= {arXiv preprint arXiv:2004.14524},
year = {2021}
}
Comments
EMNLP 2020 camera ready