Distinguish Confusing Law Articles for Legal Judgment Prediction (2020)

Abstract: Legal Judgement Prediction (LJP) is the task of automatically predicting a law case’s judgment results given a text describing the case’s facts, which has great prospects in judicial assistance systems and handy services for the public. In practice, confusing charges are often presented, because law cases applicable to similar law articles are easily misjudged. To address this issue, existing work relies heavily on domain experts, which hinders its application in different law systems. In this paper, we present an end-to-end model, LADAN, to solve the task of LJP. To distinguish confusing charges, we propose a novel graph neural network, GDL, to automatically learn subtle differences between confusing law articles, and also design a novel attention mechanism that fully exploits the learned differences to attentively extract effective discriminative features from fact descriptions. Experiments conducted on real-world datasets demonstrate the superiority of our LADAN.

## introduction exploiting artificial intelligence techniques to assist legal judgment has become popular in recent years. legal judgment prediction (ljp) aims to predict a case's judgment results, such as applicable law articles, charges, and terms of penalty, based on its fact description, as illustrated in figure 1 . ljp can assist judiciary workers in processing cases and offer legal consultancy services to the public. in the literature, ljp is usually formulated as a text classification problem, and several rule-based methods @xcite and neural-based methods @xcite @xcite have been proposed. the main drawback of existing methods is that they cannot solve the confusing charges issue. at 18:00 on october 26, 2015, the defendant zhao xx and zhang xx had an altercation. zhao xx beat up zhang and caused injuries. after identification, the injuries of bilateral nasal bone fractures of zhang xx were minor injuries of grade ii…… law article 234:[the crime of intentional injury]whoever intentionally injures another person shall be sentenced to fixed-term imprisonment of not more than three years, criminal detention or public surveillance…… ## related work our work solves the problem of the confusing charge in the ljp task by referring to the calculation principle of graph neural network (gnn). therefore, in this section, we will introduce related works from these two aspects. ## problem formulation in this section, we introduce some notations and terminologies, and then formulate the ljp task. law cases. each law case consists of a fact description and several judgment results (cf. figure 1 ). the fact description is represented as a text document, denoted by f . the judgment results may include applicable law articles, charges, terms of penalty, etc. assume there are t kinds of judgment results, and the i-th judgment result is represented as a categorical variable y i which takes value from set y i . then, a law case can be represented by a tuple (f, y 1 , . . . , y t ). law articles. law cases are often analyzed and adjudicated according to a legislature's statutory law (also known as, written law). formally, we denote the statutory law as a set of law articles l {l 1 , . . . , l m } where m is the number of law articles. similar to the fact description of cases, we also represent each law article l i as a document. legal judgment prediction. in this paper, we consider three kinds of judgment results: applicable law articles, charges, and terms of penalty. given a training dataset d {(f, y 1 , y 2 , y 3 ) z } q @xmath0, we aim to train a model f(•) that can predict the judgment results for any test law case with a fact description f test , i.e., f(f test , l) = (ŷ 1 , ŷ2 , ŷ3 ), where ŷi ∈ y i , @xmath1, 2, 3. following @xcite , we assume each case has only one applicable law article. 4 our method ## conclusion in this paper, we present an end-to-end model, ladan, to solve the issue of confusing charges in ljp. in ladan, a novel attention mechanism is proposed to extract the key features for distinguishing confusing law articles attentively. our attention mechanism not only considers the interaction between fact description and law articles but also the differences among similar law articles, which are effectively extracted by a graph neural network gdl proposed in this paper. the experimental results on real-world datasets show that our ladan raises the f1-score of state-of-the-art by up to 5.79%. in the future, we plan to study complicated situations such as a law case with multiple defendants and charges.