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Found 22 papers, 5 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.
Towards Explainability and Fairness in Swiss Judgement Prediction: Benchmarking on a Multilingual Dataset
The assessment of explainability in Legal Judgement Prediction (LJP) systems is of paramount importance in building trustworthy and transparent systems, particularly considering the reliance of these systems on factors that may lack legal relevance or involve sensitive attributes.
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.
Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents
In this work, we reformulate the task at span level as identifying spans of multiple consecutive sentences that share the same rhetorical role label to be assigned via classification.
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.
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.
Attack on Unfair ToS Clause Detection: A Case Study using Universal Adversarial Triggers
Recent work has demonstrated that natural language processing techniques can support consumer protection by automatically detecting unfair clauses in the Terms of Service (ToS) Agreement.
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.
Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance
Summarizing legal decisions requires the expertise of law practitioners, which is both time- and cost-intensive.
Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains
In this paper, we examine the use of multi-lingual sentence embeddings to transfer predictive models for functional segmentation of adjudicatory decisions across jurisdictions, legal systems (common and civil law), languages, and domains (i. e. contexts).
Context-Aware Legal Citation Recommendation using Deep Learning
Lawyers and judges spend a large amount of time researching the proper legal authority to cite while drafting decisions.
Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks
Pre-trained word embeddings are the primary method for transfer learning in several Natural Language Processing (NLP) tasks.
Question Answering via Web Extracted Tables and Pipelined Models
Each QA instance comprises a table of either kind, a natural language question, and a corresponding structured SQL query.
Towards Inference-Oriented Reading Comprehension: ParallelQA
In this paper, we investigate the tendency of end-to-end neural Machine Reading Comprehension (MRC) models to match shallow patterns rather than perform inference-oriented reasoning on RC benchmarks.
How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing
Wang et al. (2015) proposed a method to build semantic parsing datasets by generating canonical utterances using a grammar and having crowdworkers paraphrase them into natural wording.
