MvSR-NAT: Multi-view Subset Regularization for Non-Autoregressive Machine Translation
Pan Xie, Zexian Li, Zheng Zhao, Jiaqi Liu, Xiaohui Hu
Abstract
Conditional masked language models (CMLM) have shown impressive progress in non-autoregressive machine translation (NAT). They learn the conditional translation model by predicting the random masked subset in the target sentence. Based on the CMLM framework, we introduce Multi-view Subset Regularization (MvSR), a novel regularization method to improve the performance of the NAT model. Specifically, MvSR consists of two parts: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">shared mask consistency</i> : we forward the same target with different mask strategies, and encourage the predictions of shared mask positions to be consistent with each other. (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">model consistency</i> , we maintain an exponential moving average of the model weights, and enforce the predictions to be consistent between the average model and the online model. Without changing the CMLM-based architecture, our approach achieves remarkable performance on three public benchmarks with 0.7-1.15 BLEU gains over previous NAT models. And, we reduce the gap to the stronger Transformer baseline.