Legal Judgment Prediction via Event Extraction with Constraints (2022)

Abstract: While significant progress has been made on the task of Legal Judgment Prediction (LJP) in recent years, the incorrect predictions made by SOTA LJP models can be attributed in part to their failure to (1) locate the key event information that determines the judgment, and (2) exploit the cross-task consistency constraints that exist among the subtasks of LJP. To address these weaknesses, we propose EPM, an Event-based Prediction Model with constraints, which surpasses existing SOTA models in performance on a standard LJP dataset.

## introduction legal judgment prediction (ljp) is a crucial task in the legal judgment decision making process. given the facts of a legal case, the goal is to predict the court's outcome. so far, english ljp has focused on predicting law articles @xcite and court decisions @xcite while french ljp @xcite has focused on predicting court rulings. in this paper, we examine ljp in the context of chinese via the widely used cail dataset @xcite , which involves three subtasks: predicting (1) law articles, (2) charges and (3) terms of penalty, as shown in figure 1 . while state-of-the-art (sota) ljp models have several fundamental limitations @xcite , one of the technical issues they face concerns their failure to locate the key event information that determines the judgment results. consider figure 2 , where the fact statement of a robbery case involves the illegal break-in description. existing models wrongly predict that the law article is about illegal search since many words describe the break-in process even though the main point is about robbery. how can we address this problem? recall that in the continental judicial system, a law article consists of two parts: (1) the event pattern, which stipulates the behavior that violates the law, and (2) law article ## baseline ljp model we begin by designing a multi-task legal judgment prediction model, which we will use as a baseline and augment with event extraction and constraints in subsequent sections. the framework of our model is shown in figure 3 . ## improving ljp via event extraction in this section, we propose a novel method to leverage event extraction to improve ljp. ## conclusion we proposed the first model that uses event extraction and hand-crafted constraints to improve ljp, achieving sota results. to facilitate future research, we make our codes and annotations publicly available at https://github.com/wapay/epm . object: the items involved in the crime. attribute: the abstract characterization of items, such as value, length.