3 years ago

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

Jindřich Libovický, Jindřich Helcl

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.

Publisher URL: http://arxiv.org/abs/1811.04719

DOI: arXiv:1811.04719v1

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