Paper: Semantic Role Features for Machine Translation

ACL ID C10-1081
Title Semantic Role Features for Machine Translation
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010

We propose semantic role features for a Tree-to-String transducer to model the re- ordering/deletion of source-side semantic roles. These semantic features, as well as the Tree-to-String templates, are trained based on a conditional log-linear model and are shown to significantly outperform systems trained based on Max-Likelihood and EM. We also show significant im- provement in sentence fluency by using the semantic role features in the log-linear model, based on manual evaluation.