An Element is Worth a Thousand Words: Enhancing Legal Case Retrieval by Incorporating Legal Elements (2024)

Abstract: Legal case retrieval plays an important role in promoting judicial justice and fairness. One of its greatest challenges is that the definition of relevance goes far beyond the common semantic relevance as in ad-hoc retrieval. In this paper, we reveal that the legal elements, which typically comprise key facts in a specialized legal context, can largely improve the relevance matching of legal case retrieval. To facilitate the use of legal elements, we construct a Chinese legal element dataset called LeCaRD-Elem based on the widely-used LeCaRD dataset, through a two-stage semi-automatic method with a minimized reliance on human labor. Meanwhile, we introduce two new models to enhance legal search using legal elements. The first, Elem4LCR-E, is a two-stage model that explicitly predicts legal elements from texts and then leverages them for improved ranking. Recognizing the potential benefits of more seamless integration, we further propose an end-to-end model called Elem4LCR-I, which internalizes the legal element knowledge into its model parameters using a tailored teacher-student training framework. Extensive experiments underscore the significant value of legal elements and demonstrate the superiority of our two proposed models in enhancing legal search over existing methods.

## introduction legal case retrieval is a crucial part of legal ai, aiming to find the most relevant cases in a case collection for a given query case. in recent years, legal case retrieval has gained increased attention due to the growing significance of the "similar cases, similar decisions" principle in various countries @xcite . ## legal element annotation in this section, we introduce our two-stage semiautomic approach for curating our lecard-elem dataset, including a data mining stage and an expert annotation stage. the overview of the data curation process is illustrated in figure 2 . ## element-aware legal case retrieval in this section, we present two approaches for enhancing legal case retrieval by integrating the knowledge of legal elements. ## conclusion in this paper, we contribute a new legal element dataset (i.e., lecard-elem) by annotating the legal elements of the widely-used lecard dataset using an efficient semi-automatic annota-tion method. based on the proposed lecard-elem dataset, we take the first step to explore the incorporation of legal element knowledge for enhancing legal case retrieval by proposing two legal element-aware ranking models (i.e., elem4lcr-e and elem4lcr-i). experimental results demonstrate superior ranking performance of our proposed models over existing baselines.