In common law jurisdictions, legal practitioners rely on precedents to
construct arguments, in line with the doctrine of \emph{stare decisis}. As the
number of cases grow over the years, prior case retrieval (PCR) has garnered
significant attention. Besides lacking real-world scale, existing PCR datasets
do not simulate a realistic setting, because their queries use complete case
documents while only masking references to prior cases. The query is thereby
exposed to legal reasoning not yet available when constructing an argument for
an undecided case as well as spurious patterns left behind by citation masks,
potentially short-circuiting a comprehensive understanding of case facts and
legal principles. To address these limitations, we introduce a PCR dataset
based on judgements from the European Court of Human Rights (ECtHR), which
explicitly separate facts from arguments and exhibit precedential practices,
aiding us to develop this PCR dataset to foster systems' comprehensive
understanding. We benchmark different lexical and dense retrieval approaches
with various negative sampling strategies, adapting them to deal with long text
sequences using hierarchical variants. We found that difficulty-based negative
sampling strategies were not effective for the PCR task, highlighting the need
for investigation into domain-specific difficulty criteria. Furthermore, we
observe performance of the dense models degrade with time and calls for further
research into temporal adaptation of retrieval models. Additionally, we assess
the influence of different views , Halsbury's and Goodhart's, in practice in
ECtHR jurisdiction using PCR task.

基于欧洲人权法院的判决，推出了一种先例检索（PCR）数据集，以帮助系统全面理解法律事实和原则。通过采用不同的文本检索方法和负采样策略，进一步研究了 PCR 任务的困难度，以及与时间相关的检索模型的性能衰减。此外，还评估了 Halsbury's 和 Goodhart's 在 ECtHR 司法界的不同观点对 PCR 任务的影响。

欧洲人权法庭先例理解与先前案例检索数据集

ECtHR-PCR: A Dataset for Precedent Understanding and Prior Case  Retrieval in the European Court of Human Rights

The task of Prior Case Retrieval (PCR) in the legal domain is about
automatically citing relevant (based on facts and precedence) prior legal cases
in a given query case. To further promote research in PCR, in this paper, we
propose a new large benchmark (in English) for the PCR task: IL-PCR (Indian
Legal Prior Case Retrieval) corpus. Given the complex nature of case relevance
and the long size of legal documents, BM25 remains a strong baseline for
ranking the cited prior documents. In this work, we explore the role of events
in legal case retrieval and propose an unsupervised retrieval method-based
pipeline U-CREAT (Unsupervised Case Retrieval using Events Extraction). We find
that the proposed unsupervised retrieval method significantly increases
performance compared to BM25 and makes retrieval faster by a considerable
margin, making it applicable to real-time case retrieval systems. Our proposed
system is generic, we show that it generalizes across two different legal
systems (Indian and Canadian), and it shows state-of-the-art performance on the
benchmarks for both the legal systems (IL-PCR and COLIEE corpora).

本文提出了一个新的国际优化的先前案例检索方法 (IL-PCR)，通过构建一种基于事件提取的无监督检索方法（U-CREAT）, 与 BM25 相比，结果显示 U-CREAT 显著提高了检索性能，并使检索速度显着更快