Paper: A Smorgasbord Of Features For Statistical Machine Translation

ACL ID N04-1021
Title A Smorgasbord Of Features For Statistical Machine Translation
Venue Human Language Technologies
Session Main Conference
Year 2004
Authors

We describe a methodology for rapid exper- imentation 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 eval- uation metric on held-out data. We present re- sults for a small selection of features at each level of syntactic representation.