Incorporating Precedents for Legal Judgement Prediction on E uropean Court of Human Rights Cases (2024)

Abstract: Inspired by the legal doctrine of stare decisis, which leverages precedents (prior cases) for informed decision-making, we explore methods to integrate them into LJP models. To facilitate precedent retrieval, we train a retriever with a fine-grained relevance signal based on the overlap ratio of alleged articles between cases. We investigate two strategies to integrate precedents: direct incorporation at inference via label interpolation based on case proximity and during training via a precedent fusion module using a stacked-cross attention model. We employ joint training of the retriever and LJP models to address latent space divergence between them. Our experiments on LJP tasks from the ECHR jurisdiction reveal that integrating precedents during training coupled with joint training of the retriever and LJP model, outperforms models without precedents or with precedents incorporated only at inference, particularly benefiting sparser articles.

## introduction the task of case outcome classification deals with identifying the outcome from a textual description of case facts and is generally referred to as legal judgement prediction (ljp) (e.g., @xcite . it has been studied using corpora from different jurisdictions, such as the european court of human rights (echr) @xcite @xcite @xcite @xcite , chinese criminal courts @xcite @xcite @xcite , us supreme court @xcite , indian supreme court @xcite , french court of cassation @xcite , federal supreme court of switzerland @xcite , turkish constitutional court @xcite uk courts @xcite and german courts @xcite . in this study, we focus on classifying case outcomes in the echr a and b benchmark tasks introduced by lexglue @xcite . task b involves identifying the set of articles of the european convention of human rights alleged to have been violated by the claimant, while task a aims to classify which of the convention's articles has been deemed violated by the court. both tasks utilize the fact description of the case extracted from the published judgement document as input. early approaches to ljp relied on rule-based methods @xcite @xcite , followed by classification techniques using bag-ofwords features @xcite . recent advancements involve deep learning @xcite @xcite with adoption of pre-trained transformer models @xcite , including legal-domain-specific variants @xcite . furthermore, various strategies are explored, such as leveraging dependencies between auxiliary tasks @xcite @xcite or incorporating constraints like contrastive learning @xcite and injecting legal knowledge @xcite . drawing inspiration from the legal doctrine of stare decisis, where precedents (prior cases decided in courts of law) are pivotal in common law jurisdictions to support arguments to arrive at the final outcome. even in civil law systems, though the prior cases are not directly involved in the final judgment, they are still crucial references during the decision-making process to ensure consistency and proper application of law. we explore how to leverage precedents in ljp models to predict the outcome of a query case. while previous research has focused on using precdents for enhancing case representations through contrastive learning @xcite @xcite , our work investigates two strategies for integrat-ing precedents: (i) direct incorporation at inference through interpolation and (ii) integration during the training process via a fusion layer, enabling the model to reason with similar cases to derive the outcome of the query case, unlike prior works which use similar cases in the form of exemplars in incontext learning to facilitate llms in a zero-shot setting @xcite . to enhance relevant precedent retrieval in both strategies, we train a retriever with a fine-grained relevance signal based on the overlap ratio of alleged articles between two cases. for direct integration at inference, we perform label distribution interpolation across each article with the retrieved cases' outcomes, determined by their proximity to the query case. this interpolation has been widely explored in retrieval-augmented knn-based language models @xcite @xcite , providing memorization capabilities for rare patterns that are otherwise challenging to capture within a parametric model. additionally, ljp models struggle to effectively leverage retrieved precedents as they are not explicitly trained to relate the query case to retrieved documents and conduct reasoning based on them. moreover, they focus on memorization during training rather than offloading this process to the retrieval component. to address this, we introduce a precedent fusion module that incorporates precedents during training via stacked cross-attention. further, we propose joint training that optimizes the retriever model alongside the ljp model, utilizing the relevance signal derived from the fusion module, helping to overcome the latent space divergence issue with static retrievers @xcite . our experiments demonstrate that integrating precedents at training time along with joint training outperform models with out precedents and with precedents at inference only and without joint training, with larger improvements for sparser articles. ## method: incorporating precedents we describe our baseline model which takes the case fact description x and outputs the set of articles as multi-hot vectors (alleged ones in case of task b and violated ones in task a). then we outline the retriever to obtain precedent cases based on the current case. we then introduce two strategies for incorporating information from precedent cases: one during inference and the other during training. ## conclusion we enhance echr outcome classification by integrating precedents into ljp models during training. this improvement is driven by three components: (i) an effective retriever trained with fine-grained relevance signals of allegation labels, (ii) precedent fusion models enabling the offloading of memorization to the retrieval step and reasoning with cues from precedents and (iii) joint training of the retriever alongside the ljp model, enhancing its representations. these precedents also equips models to provide explanations through analogous casebased reasoning, warranting further investigation.