Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach

@inproceedings{10.1145/3626772.3657717,
author = {Qin, Weicong and Cao, Zelin and Yu, Weijie and Si, Zihua and Chen, Sirui and Xu, Jun},
title = {Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach},
year = {2024},
booktitle = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},
}

Legal case retrieval and judgment prediction are crucial components in intelligent legal systems. In practice, determining whether two cases share the same charges through legal judgment prediction is essential for establishing their relevance in case retrieval. However, current studies on legal case retrieval merely focus on the semantic similarity between paired cases, ignoring their charge-level consistency. This separation leads to a lack of context and potential inaccuracies in the case retrieval that can undermine trust in the system's decision-making process. Given the guidance role of laws to both tasks and inspired by the success of generative retrieval, in this work, we propose to incorporate judgment prediction into legal case retrieval, achieving a novel law-aware Generative legal case retrieval method called Gear. Specifically, Gear first extracts rationales (key circumstances and key elements) for legal cases according to the definition of charges in laws, ensuring a shared and informative representation for both tasks. Then in accordance with the inherent hierarchy of laws, we construct a law structure constraint tree and assign law-aware semantic identifier(s) to each case based on this tree. These designs enable a unified traversal from the root, through intermediate charge nodes, to case-specific leaf nodes, which respectively correspond to two tasks. Additionally, in the training, we also introduce a revision loss that jointly minimizes the discrepancy between the identifiers of predicted and labeled charges as well as retrieved cases, improving the accuracy and consistency for both tasks. Extensive experiments on two datasets demonstrate that Gear consistently outperforms state-of-the-art methods in legal case retrieval while maintaining competitive judgment prediction performance.