Semantic Role Features for Machine Translation

We propose semantic role features for a Tree-to-String transducer to model the reordering/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 improvement in sentence fluency by using the semantic role features in the log-linear model, based on manual evaluation.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Ding Liu, Daniel Gildea
• Keywords: COLING 2010, Computational Linguistics, Log-linear Model, Semantic Role, Source-side Semantic Roles