Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation
Pengzhi Gao, Zhongjun He, Hua Wu, Haifeng Wang
Abstract
We introduce Bi-SimCut: a simple but effective training strategy to boost neural machine translation (NMT) performance. It consists of two procedures: bidirectional pretraining and unidirectional finetuning. Both procedures utilize SimCut, a simple regularization method that forces the consistency between the output distributions of the original and the cutoff sentence pairs. Without leveraging extra dataset via back-translation or integrating large-scale pretrained model, Bi-SimCut achieves strong translation performance across five translation benchmarks (data sizes range from 160K to 20.2M): BLEU scores of 31.16 for en→de and 38.37 for de→en on the IWSLT14 dataset, 30.78 for en→de and 35.15 for de→en on the WMT14 dataset, and 27.17 for zh→en on the WMT17 dataset. SimCut is not a new method, but a version of Cutoff (Shen et al., 2020) simplified and adapted for NMT, and it could be considered as a perturbation-based method. Given the universality and simplicity of Bi-SimCut and SimCut, we believe they can serve as strong baselines for future NMT research.
BibTeX
@inproceedings{gao-etal-2022-bi,
title = "{B}i-{S}im{C}ut: A Simple Strategy for Boosting Neural Machine Translation",
author = "Gao, Pengzhi and
He, Zhongjun and
Wu, Hua and
Wang, Haifeng",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.289/",
doi = "10.18653/v1/2022.naacl-main.289",
pages = "3938--3948"
}