  Autoregressive decoding is the only part of sequence-to-sequence models that
prevents them from massive parallelization at inference time.
Non-autoregressive models enable the decoder to generate all output symbols
independently in parallel. We present a novel non-autoregressive architecture
based on connectionist temporal classification and evaluate it on the task of
neural machine translation. Unlike other non-autoregressive methods which
operate in several steps, our model can be trained end-to-end. We conduct
experiments on the WMT English-Romanian and English-German datasets. Our models
achieve a significant speedup over the autoregressive models, keeping the
translation quality comparable to other non-autoregressive models.
