  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 SimCut and Bi-SimCut, we believe they can serve as strong
baselines for future NMT research.
