Dispute Generation in Law Documents via Joint Context and Topic Attention

In this paper, we study the Dispute Generation (DG) problem from the plaintiff allegation (PA) and the defendant argument (DA) in a law document. We are the first to formulate DG as a text-to-text natural language generation (NLG) problem. Since the logical relationships between a PA and a DA are rather difficult to identify, existing models cannot generate accurate disputes, let alone find all disputes. To solve this problem, we propose a novel Seq2Seq model with two dispute detection modules, which captures relationships among the PA and the DA in two ways. First, in the context-level detection module, we employ hierarchical attention mechanism to learn sentence representation and joint attention mechanism to match right disputes. Second, in the topic-level detection module, topic information is taken into account to find indirect disputes. We conduct extensive experiments on the real-world dataset. The results demonstrate the effectiveness of our method. Also the results show that the context-level and the topic-level detection modules can improve the accuracy and coverage of generated disputes.