Non-autoregressive neural machine translation (NAT) predicts the entire target sequence simultaneously and significantly accelerates inference process. However, NAT discards the dependency information in a sentence, and thus inevitably suffers from the multi-modality problem: the target tokens may be provided by different possible translations, often causing token repetitions or missing. To alleviate this problem, we propose a novel semi-autoregressive model RecoverSAT in this work, which generates a translation as a sequence of segments. The segments are generated simultaneously while each segment is predicted token-by-token. By dynamically determining segment length and deleting repetitive segments, RecoverSAT is capable of recovering from repetitive and missing token errors. Experimental results on three widely-used benchmark datasets show that our proposed model achieves more than 4$\times$ speedup while maintaining comparable performance compared with the corresponding autoregressive model.

提出一种称为RecoverSAT的半自回归模型，用于非自回归神经机器翻译，其将翻译分成段并同时生成，同时每个段是逐个生成记号来预测的，通过动态确定段长度并删除重复部分，能够解决重复和缺失记号的多模态问题。与相应的自回归模型相比，该模型在保持相当性能的同时，实现了4倍以上的加速。

非自回归神经机器翻译多模态错误恢复的学习