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» Learning Rules to Improve a Machine Translation System
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ACL
2009
14 years 9 months ago
Collaborative Decoding: Partial Hypothesis Re-ranking Using Translation Consensus between Decoders
This paper presents collaborative decoding (co-decoding), a new method to improve machine translation accuracy by leveraging translation consensus between multiple machine transla...
Mu Li, Nan Duan, Dongdong Zhang, Chi-Ho Li, Ming Z...
ACL
2006
15 years 1 months ago
Statistical Phrase-Based Models for Interactive Computer-Assisted Translation
Obtaining high-quality machine translations is still a long way off. A postediting phase is required to improve the output of a machine translation system. An alternative is the s...
Jesús Tomás, Francisco Casacuberta
AIR
2006
107views more  AIR 2006»
14 years 11 months ago
Just enough learning (of association rules): the TAR2 "Treatment" learner
Abstract. An over-zealous machine learner can automatically generate large, intricate, theories which can be hard to understand. However, such intricate learning is not necessary i...
Tim Menzies, Ying Hu
ACL
2012
13 years 2 months ago
Prediction of Learning Curves in Machine Translation
Parallel data in the domain of interest is the key resource when training a statistical machine translation (SMT) system for a specific purpose. Since ad-hoc manual translation c...
Prasanth Kolachina, Nicola Cancedda, Marc Dymetman...
LREC
2008
128views Education» more  LREC 2008»
15 years 1 months ago
Automatic Evaluation Measures for Statistical Machine Translation System Optimization
Evaluation of machine translation (MT) output is a challenging task. In most cases, there is no single correct translation. In the extreme case, two translations of the same input...
Arne Mauser, Sasa Hasan, Hermann Ney