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NAACL
2004

A Smorgasbord of Features for Statistical Machine Translation

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A Smorgasbord of Features for Statistical Machine Translation
We describe a methodology for rapid experimentation in statistical machine translation which we use to add a large number of features to a baseline system exploiting features from a wide range of levels of syntactic representation. Feature values were combined in a log-linear model to select the highest scoring candidate translation from an n-best list. Feature weights were optimized directly against the BLEU evaluation metric on held-out data. We present results for a small selection of features at each level of syntactic representation.
Franz Josef Och, Daniel Gildea, Sanjeev Khudanpur,
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NAACL
Authors Franz Josef Och, Daniel Gildea, Sanjeev Khudanpur, Anoop Sarkar, Kenji Yamada, Alexander Fraser, Shankar Kumar, Libin Shen, David Smith, Katherine Eng, Viren Jain, Zhen Jin, Dragomir R. Radev
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