A smorgasbord of features for statistical machine translation.

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Human Language Technology and 5th Meeting of the NAACL. 2004 May

Och FJ, Gildea D, Khudanpur S, Sarkar A, Yamada K, Fraser A, Kumar S, Shen L, Smith D, Eng K, Jain V, Jin Z, Radev D

Note: Research in this publication was not performed at Janelia.

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Abstract

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.

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A smorgasbord of features for statistical machine translation.

Note: Research in this publication was not performed at Janelia.

Abstract