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Found 12 papers, 3 papers with code
CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval
In this paper, we introduce CuSINeS, a negative sampling approach to enhance the performance of Statutory Article Retrieval (SAR).
Mind Your Neighbours: Leveraging Analogous Instances for Rhetorical Role Labeling for Legal Documents
Rhetorical Role Labeling (RRL) of legal judgments is essential for various tasks, such as case summarization, semantic search and argument mining.
ECtHR-PCR: A Dataset for Precedent Understanding and Prior Case Retrieval in the European Court of Human Rights
In common law jurisdictions, legal practitioners rely on precedents to construct arguments, in line with the doctrine of \emph{stare decisis}.
Query-driven Relevant Paragraph Extraction from Legal Judgments
We notice that the legal pre-training handles distribution shift on the corpus side but still struggles on query side distribution shift, with unseen legal queries.
LexAbSumm: Aspect-based Summarization of Legal Decisions
Legal professionals frequently encounter long legal judgments that hold critical insights for their work.
Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization
In particular, we investigate whether supplementing models with unlabeled target jurisdiction corpus and extractive silver summaries obtained from unsupervised algorithms on target data enhances transfer performance.
Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification
We observe limited alignment with the judge vote distribution.
From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification
Overall, our case study reveals hitherto underappreciated complexities in creating benchmark datasets in legal NLP that revolve around identifying aspects of a case's facts supposedly relevant to its outcome.
VECHR: A Dataset for Explainable and Robust Classification of Vulnerability Type in the European Court of Human Rights
Our results demonstrate the challenging nature of the task with lower prediction performance and limited agreement between models and experts.
Leveraging Task Dependency and Contrastive Learning for Case Outcome Classification on European Court of Human Rights Cases
We report on an experiment in case outcome classification on European Court of Human Rights cases where our model first learns to identify the convention articles allegedly violated by the state from case facts descriptions, and subsequently uses that information to classify whether the court finds a violation of those articles.
Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases
In this paper, we cast Legal Judgment Prediction on European Court of Human Rights cases into an article-aware classification task, where the case outcome is classified from a combined input of case facts and convention articles.
Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts
This work demonstrates that Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals that arise from corpus construction, case distribution, and confounding factors.
