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

Jindřich Libovický, Jindřich Helcl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Place of PublicationBrussels, Belgium
PublisherAssociation for Computational Linguistics
Pages3016-3021
Number of pages6
ISBN (Electronic)978-1-948087-84-1
DOIs
Publication statusPublished - 1 Oct 2018
Event2018 Conference on Empirical Methods in Natural Language Processing - Square Meeting Center, Brussels, Belgium
Duration: 31 Oct 20184 Nov 2018
http://emnlp2018.org/

Conference

Conference2018 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2018
Country/TerritoryBelgium
CityBrussels
Period31/10/184/11/18
Internet address

Fingerprint

Dive into the research topics of 'End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification'. Together they form a unique fingerprint.

Cite this