  Non-autoregressive translation (NAT) significantly accelerates the inference
process via predicting the entire target sequence. However, recent studies show
that NAT is weak at learning high-mode of knowledge such as one-to-many
translations. We argue that modes can be divided into various granularities
which can be learned from easy to hard. In this study, we empirically show that
NAT models are prone to learn fine-grained lower-mode knowledge, such as words
and phrases, compared with sentences. Based on this observation, we propose
progressive multi-granularity training for NAT. More specifically, to make the
most of the training data, we break down the sentence-level examples into three
types, i.e. words, phrases, sentences, and with the training goes, we
progressively increase the granularities. Experiments on Romanian-English,
English-German, Chinese-English, and Japanese-English demonstrate that our
approach improves the phrase translation accuracy and model reordering ability,
therefore resulting in better translation quality against strong NAT baselines.
Also, we show that more deterministic fine-grained knowledge can further
enhance performance.
