NYU-MILA Neural Machine Translation Systems for WMT'16

Junyoung Chung, Kyunghyun Cho, Yoshua Bengio

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

Abstract

We describe the neural machine translation system of New York University (NYU) and University of Montreal (MILA) for the translation tasks of WMT'16. The main goal of NYU-MILA submission to WMT'16 is to evaluate a new character-level decoding approach in neural machine translation on various language pairs. The proposed neural machine translation system is an attention-based encoder-decoder with a subword-level encoder and a character-level decoder. The decoder of the neural machine translation system does not require explicit segmentation, when characters are used as tokens. The character-level decoding approach provides benefits especially when translating a source language into other morphologically rich languages.

Original languageEnglish (US)
Title of host publicationShared Task Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages268-271
Number of pages4
ISBN (Electronic)9781945626104
StatePublished - 2016
Event1st Conference on Machine Translation, WMT 2016, held at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Berlin, Germany
Duration: Aug 7 2016Aug 12 2016

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
Volume2
ISSN (Print)0736-587X

Conference

Conference1st Conference on Machine Translation, WMT 2016, held at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
Country/TerritoryGermany
CityBerlin
Period8/7/168/12/16

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

Fingerprint

Dive into the research topics of 'NYU-MILA Neural Machine Translation Systems for WMT'16'. Together they form a unique fingerprint.

Cite this