@inproceedings{libovicky-helcl-2018-end,
title = "End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification",
author = "Libovick{\'y}, Jind{\v{r}}ich and
Helcl, Jind{\v{r}}ich",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1336",
doi = "10.18653/v1/D18-1336",
pages = "3016--3021",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification
%A Libovický, Jindřich
%A Helcl, Jindřich
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F libovicky-helcl-2018-end
%X 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.
%R 10.18653/v1/D18-1336
%U https://aclanthology.org/D18-1336
%U https://doi.org/10.18653/v1/D18-1336
%P 3016-3021
Markdown (Informal)
[End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification](https://aclanthology.org/D18-1336) (Libovický & Helcl, EMNLP 2018)
ACL