As an important component of intelligent legal systems, legal case retrieval
plays a critical role in ensuring judicial justice and fairness. However, the
development of legal case retrieval technologies in the Chinese legal system is
restricted by three problems in existing datasets: limited data size, narrow
definitions of legal relevance, and naive candidate pooling strategies used in
data sampling. To alleviate these issues, we introduce LeCaRDv2, a large-scale
Legal Case Retrieval Dataset (version 2). It consists of 800 queries and 55,192
candidates extracted from 4.3 million criminal case documents. To the best of
our knowledge, LeCaRDv2 is one of the largest Chinese legal case retrieval
datasets, providing extensive coverage of criminal charges. Additionally, we
enrich the existing relevance criteria by considering three key aspects:
characterization, penalty, procedure. This comprehensive criteria enriches the
dataset and may provides a more holistic perspective. Furthermore, we propose a
two-level candidate set pooling strategy that effectively identify potential
candidates for each query case. It's important to note that all cases in the
dataset have been annotated by multiple legal experts specializing in criminal
law. Their expertise ensures the accuracy and reliability of the annotations.
We evaluate several state-of-the-art retrieval models at LeCaRDv2,
demonstrating that there is still significant room for improvement in legal
case retrieval. The details of LeCaRDv2 can be found at the anonymous website
this https URL

智能法律系统中的重要组成部分，法律案例检索在确保司法公正和公平方面起着关键作用。为了解决现有数据集中的三个问题，本研究介绍了一个大规模的法律案例检索数据集 LeCaRDv2，并丰富了现有的相关性标准，同时提出了一个有效识别潜在候选项的候选集合策略。在 LeCaRDv2 上评估了多个检索模型，表明法律案例检索仍有很大的改进空间。

LeCaRDv2：一个大规模的中文法律案例检索数据集

LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset

Charge prediction, determining charges for criminal cases by analyzing the
textual fact descriptions, is a promising technology in legal assistant
systems. In practice, the fact descriptions could exhibit a significant
intra-class variation due to factors like non-normative use of language, which
makes the prediction task very challenging, especially for charge classes with
too few samples to cover the expression variation. In this work, we explore to
use the charge definitions from criminal law to alleviate this issue. The key
idea is that the expressions in a fact description should have corresponding
formal terms in charge definitions, and those terms are shared across classes
and could account for the diversity in the fact descriptions. Thus, we propose
to create auxiliary fact representations from charge definitions to augment
fact descriptions representation. The generated auxiliary representations are
created through the interaction of fact description with the relevant charge
definitions and terms in those definitions by integrated sentence- and
word-level attention scheme. Experimental results on two datasets show that our
model achieves significant improvement than baselines, especially for classes
with few samples.

通过使用刑法条文的定义，基于刑法条款中的正式术语来解决不同罪案之间表达差异的问题，用于辅助法律助手系统中的罪案预测技术，并使用集成的句子和单词级别的注意机制来创建辅助事实表示。实验结果表明，我们的模型在类别数据有限的情况下，有望显著提高辅助系统的预测性能。