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Non-autoregressive neural machine translation (NART) models suffer from themulti-modality problem which causes translation inconsistency such as tokenrepetition. Most recent approaches have attempted to solve this problem byimplicitly modeling dependencies between outputs. In this paper, we introduceAligNART, which leverages full alignment information to explicitly reduce themodality of the target distribution. AligNART divides the machine translationtask into $(i)$ alignment estimation and $(ii)$ translation with aligneddecoder inputs, guiding the decoder to focus on simplified one-to-onetranslation. To alleviate the alignment estimation problem, we further proposea novel alignment decomposition method. Our experiments show that AligNARToutperforms previous non-iterative NART models that focus on explicit modalityreduction on WMT14 En$\leftrightarrow$De and WMT16 Ro$\rightarrow$En.Furthermore, AligNART achieves BLEU scores comparable to those of thestate-of-the-art connectionist temporal classification based models on WMT14En$\leftrightarrow$De. We also observe that AligNART effectively addresses thetoken repetition problem even without sequence-level knowledge distillation.

AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate