Improving Non-autoregressive Machine Translation with Error Exposure and Consistency Regularization
This work addresses a specific bottleneck in iterative-refinement-based non-autoregressive machine translation, offering incremental improvements for machine translation systems.
The paper tackled the data distribution discrepancy between training and inference in non-autoregressive machine translation by introducing error exposure and consistency regularization, resulting in an average improvement of 0.68 and 0.40 BLEU scores on five benchmarks and achieving performance comparable to the Transformer.
Being one of the IR-NAT (Iterative-refinemennt-based NAT) frameworks, the Conditional Masked Language Model (CMLM) adopts the mask-predict paradigm to re-predict the masked low-confidence tokens. However, CMLM suffers from the data distribution discrepancy between training and inference, where the observed tokens are generated differently in the two cases. In this paper, we address this problem with the training approaches of error exposure and consistency regularization (EECR). We construct the mixed sequences based on model prediction during training, and propose to optimize over the masked tokens under imperfect observation conditions. We also design a consistency learning method to constrain the data distribution for the masked tokens under different observing situations to narrow down the gap between training and inference. The experiments on five translation benchmarks obtains an average improvement of 0.68 and 0.40 BLEU scores compared to the base models, respectively, and our CMLMC-EECR achieves the best performance with a comparable translation quality with the Transformer. The experiments results demonstrate the effectiveness of our method.