Joint Inference for Bilingual Semantic Role Labeling

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 argument 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 bilingual SRL result that is not only reasonable on each side of bitext, but also has more consistent argument structures between two sides. To evaluate the consistency between two argument 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 PropBank data. Using our joint inference model, F1 scores of SRL results on Chinese and English text achieve 79.53% and 77.87% respec

• Type: Journal
• Year: 2010
• Where: EMNLP
• Authors: Tao Zhuang, Chengqing Zong
• Keywords: Argument Structures, Bitext, EMNLP 2010, Joint Inference Model, Natural Language Processing