Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation
Pengzhi Gao, Zhongjun He, Hua Ren Wu, Haifeng Wang · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
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.Sim-Cut 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 SimCut and Bi-SimCut, we believe they can serve as strong baselines for future NMT research.