Distinguish Confusing Law Articles for Legal Judgment Prediction
This work addresses a domain-specific issue in judicial assistance systems by improving prediction accuracy for confusing charges, though it appears incremental as it builds on existing LJP methods.
The paper tackles the problem of distinguishing confusing law articles in Legal Judgment Prediction (LJP) by proposing LADAN, an end-to-end model that uses a graph neural network and attention mechanism to learn subtle differences, achieving superior performance on real-world datasets.
Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts, which has excellent prospects in judicial assistance systems and convenient services for the public. In practice, confusing charges are frequent, because law cases applicable to similar law articles are easily misjudged. For addressing this issue, the existing method 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 to automatically learn subtle differences between confusing law articles and design a novel attention mechanism that fully exploits the learned differences to extract compelling discriminative features from fact descriptions attentively. Experiments conducted on real-world datasets demonstrate the superiority of our LADAN.