Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  show that jointly performing semantic role labeling (SRL) on bitext can improve SRL results on both sides. In our approach,  use monolingual SRL systems to produce argument candidates for predicates in bitext at first. Then,  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 beten two sides. To evaluate the consistency beten two argument structures,  also formulate a log-linear model to compute the probability of aligning two arguments.  have experimented with our model on Chinese-English parallel Prop-Bank data. Using our joint inference model, F1 scores of SRL results on Chinese and English text achieve 79.53% and 77.87% respectively, which are 1.52 and 1.74 points higher than the results of baseline monolingual SRL combination systems respectively.
['Tao Zhuang', 'Chengqing Zong']
I am writing a research paper on a idea Joint Inference for Bilingual Semantic Role Labeling
[{"from": "human", "value": "I am writing a research paper on a idea Joint Inference for Bilingual Semantic Role Labeling"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  show that jointly performing semantic role labeling (SRL) on bitext can improve SRL results on both sides. In our approach,  use monolingual SRL systems to produce argument candidates for predicates in bitext at first. Then,  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 beten two sides. To evaluate the consistency beten two argument structures,  also formulate a log-linear model to compute the probability of aligning two arguments.  have experimented with our model on Chinese-English parallel Prop-Bank data. Using our joint inference model, F1 scores of SRL results on Chinese and English text achieve 79.53% and 77.87% respectively, which are 1.52 and 1.74 points higher than the results of baseline monolingual SRL combination systems respectively."}]