Paper: Joint Inference for Bilingual Semantic Role Labeling

ACL ID D10-1030
Title Joint Inference for Bilingual Semantic Role Labeling
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

We show that jointly performing semantic role labeling (SRL) on bitext can improve SRL results on both sides. In our approach, we use monolingual SRL systems to produce ar- gument candidates for predicates in bitext at first. Then, we simultaneously generate SRL results for two sides of bitext using our joint inference model. Our model prefers the bilin- gual SRL result that is not only reasonable on each side of bitext, but also has more consis- tent argument structures between two sides. To evaluate the consistency between two argu- ment structures, we also formulate a log-linear model to compute the probability of aligning two arguments. We have experimented with our model on Chinese-English parallel Prop- Bank data. Using our joint inference model, F1 scores of SRL results on Chinese a...