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Found 7 papers, 2 papers with code
The Chinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for NLI and a Probing Task
We find that the model’s performance on CANLI does not correspond to its internal representation of CPH, which is the crucial linguistic ability central to the CANLI dataset.
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.
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.
