End to End Non Autoregressive Neural Machine Translation with Connectionist Temporal Classification
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.
EMNLP