The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015

Barry Haddow; Matthias Huck; Alexandra Birch; Nikolay Bogoychev; Philipp Koehn

The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015

Barry Haddow, Matthias Huck, Alexandra Birch, Nikolay Bogoychev, Philipp Koehn

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper describes the submission of the University of Edinburgh and the Johns Hopkins University for the shared translation task of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation (WMT 2015). We set up phrase-based statistical machine translation systems for all ten language pairs of this year’s evaluation campaign, which are English paired with Czech, Finnish, French, German, and Russian in both translation directions.

Novel research directions we investigated include: neural network language models and bilingual neural network language models, a comprehensive use of word classes, and sparse lexicalized reordering features.

Original language	English
Title of host publication	Proceedings of the Tenth Workshop on Statistical Machine Translation
Place of Publication	Lisbon, Portugal
Publisher	Association for Computational Linguistics
Pages	126-133
Number of pages	8
Publication status	Published - 1 Sep 2015

Access to Document

W15-3013Final published version, 133 KB

http://aclweb.org/anthology/W15-3013

4 Finished

Translation for Massive Open Online Courses- TraMooc
Koehn, P. & Birch-Mayne, A.
EU government bodies
1/02/15 → 31/01/18
Project: Research
QT21: Quality Translation 21
Koehn, P., Haddow, B. & Huck, M.
EU government bodies
1/02/15 → 31/01/18
Project: Research
HimL: Health in my Language
Haddow, B., Birch-Mayne, A. & Webber, B.
EU government bodies
1/02/15 → 31/01/18
Project: Research

Cite this

@inproceedings{8eb09c603be6486a8654ea5d033d1818,

title = "The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015",

abstract = "This paper describes the submission of the University of Edinburgh and the Johns Hopkins University for the shared translation task of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation (WMT 2015). We set up phrase-based statistical machine translation systems for all ten language pairs of this year{\textquoteright}s evaluation campaign, which are English paired with Czech, Finnish, French, German, and Russian in both translation directions.Novel research directions we investigated include: neural network language models and bilingual neural network language models, a comprehensive use of word classes, and sparse lexicalized reordering features.",

author = "Barry Haddow and Matthias Huck and Alexandra Birch and Nikolay Bogoychev and Philipp Koehn",

year = "2015",

month = sep,

day = "1",

language = "English",

pages = "126--133",

booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",

publisher = "Association for Computational Linguistics",

}

TY - GEN

T1 - The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015

AU - Haddow, Barry

AU - Huck, Matthias

AU - Birch, Alexandra

AU - Bogoychev, Nikolay

AU - Koehn, Philipp

PY - 2015/9/1

Y1 - 2015/9/1

N2 - This paper describes the submission of the University of Edinburgh and the Johns Hopkins University for the shared translation task of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation (WMT 2015). We set up phrase-based statistical machine translation systems for all ten language pairs of this year’s evaluation campaign, which are English paired with Czech, Finnish, French, German, and Russian in both translation directions.Novel research directions we investigated include: neural network language models and bilingual neural network language models, a comprehensive use of word classes, and sparse lexicalized reordering features.

AB - This paper describes the submission of the University of Edinburgh and the Johns Hopkins University for the shared translation task of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation (WMT 2015). We set up phrase-based statistical machine translation systems for all ten language pairs of this year’s evaluation campaign, which are English paired with Czech, Finnish, French, German, and Russian in both translation directions.Novel research directions we investigated include: neural network language models and bilingual neural network language models, a comprehensive use of word classes, and sparse lexicalized reordering features.

M3 - Conference contribution

SP - 126

EP - 133

BT - Proceedings of the Tenth Workshop on Statistical Machine Translation

PB - Association for Computational Linguistics

CY - Lisbon, Portugal

ER -

The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015

Abstract

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Projects

Translation for Massive Open Online Courses- TraMooc

QT21: Quality Translation 21

HimL: Health in my Language

Cite this