Practical Neural Machine Translation

Rico Sennrich, Barry Haddow


Abstract
Neural Machine Translation (NMT) has achieved new breakthroughs in machine translation in recent years. It has dominated recent shared translation tasks in machine translation research, and is also being quickly adopted in industry. The technical differences between NMT and the previously dominant phrase-based statistical approach require that practictioners learn new best practices for building MT systems, ranging from different hardware requirements, new techniques for handling rare words and monolingual data, to new opportunities in continued learning and domain adaptation.This tutorial is aimed at researchers and users of machine translation interested in working with NMT. The tutorial will cover a basic theoretical introduction to NMT, discuss the components of state-of-the-art systems, and provide practical advice for building NMT systems.
Anthology ID:
E17-5002
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Alexandre Klementiev, Lucia Specia
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
Language:
URL:
https://aclanthology.org/E17-5002
DOI:
Bibkey:
Cite (ACL):
Rico Sennrich and Barry Haddow. 2017. Practical Neural Machine Translation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Practical Neural Machine Translation (Sennrich & Haddow, EACL 2017)
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PDF:
https://aclanthology.org/E17-5002.pdf