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