Feature-Rich Phrase-based Translation: Stanford University's Submission to the WMT 2013 Translation Task
WMT@ACL(2013)
Abstract
We describe the Stanford University NLP Group submission to the 2013 Workshop on Statistical Machine Translation Shared Task. We demonstrate the eectiveness of a new adaptive, online tuning algorithm that scales to large feature and tuning sets. For both English-French and English-German, the algorithm produces feature-rich models that improve over a dense baseline and compare favorably to models tuned with established methods.
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Key words
translation,wmt,feature-rich,phrase-based
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