In this paper, we capture the argument relationships for Chinese semantic role labeling task, and improve the task's performance with the help of argument relationships. We split the relationship between two candidate arguments into two categories: (1) Compatible arguments: if one candidate argument belongs to a given predicate, then the other is more likely to belong to the same predicate; (2) Incompatible arguments: if one candidate argument belongs to a given predicate, then the other is less likely to belong to the same predicate. However, previous works did not explicitly model argument relationships. We use a simple maximum entropy classifier to capture the two categories of argument relationships and test its performance on the Chinese Proposition Bank (CPB). The experiments show that argument relationships is effective in Chinese semantic role labeling task.