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An Empirical Comparison of Features and Tuning for Phrase-based Machine Translation
Spence Green
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Daniel Cer
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Christopher Manning
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Paper Details:
Month: June
Year: 2014
Location: Baltimore, Maryland, USA
Venue:
WMT |
WS |
SIG: SIGMT
Citations
URL
Human Effort and Machine Learnability in Computer Aided Translation
Spence Green
|
Sida I. Wang
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Jason Chuang
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Jeffrey Heer
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Sebastian Schuster
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Christopher D. Manning
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Flexible Domain Adaptation for Automated Essay Scoring Using Correlated Linear Regression
Peter Phandi
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Kian Ming A. Chai
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Hwee Tou Ng
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Hierarchical Incremental Adaptation for Statistical Machine Translation
Joern Wuebker
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Spence Green
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John DeNero
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Deep Neural Language Models for Machine Translation
Thang Luong
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Michael Kayser
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Christopher D. Manning
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A Comparison of Update Strategies for Large-Scale Maximum Expected BLEU Training
Joern Wuebker
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Sebastian Muehr
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Patrick Lehnen
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Stephan Peitz
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Hermann Ney
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BIRA: Improved Predictive Exchange Word Clustering
Jon Dehdari
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Liling Tan
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Josef van Genabith
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Phrasal: A Toolkit for New Directions in Statistical Machine Translation
Spence Green
|
Daniel Cer
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Christopher Manning
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Stanford University’s Submissions to the WMT 2014 Translation Task
Julia Neidert
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Sebastian Schuster
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Spence Green
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Kenneth Heafield
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Christopher Manning
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The QT21/HimL Combined Machine Translation System
Jan-Thorsten Peter
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Tamer Alkhouli
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Hermann Ney
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Matthias Huck
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Fabienne Braune
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Alexander Fraser
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Aleš Tamchyna
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Ondřej Bojar
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Barry Haddow
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Rico Sennrich
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Frédéric Blain
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Lucia Specia
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Jan Niehues
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Alex Waibel
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Alexandre Allauzen
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Lauriane Aufrant
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Franck Burlot
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Elena Knyazeva
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Thomas Lavergne
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François Yvon
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Mārcis Pinnis
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Stella Frank
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http://nlp.stanford.edu/software/phrasal
Field Of Study
Language
Chinese
English
Arabic
Dataset
News
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