4 citations · 4 across the 6 of their papers we have counts for
6 papers
HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents
T. Y. S. S. Santosh, Apolline Isaia, Shiyu Hong +1
Rhetorical Role Labeling (RRL) of legal documents is pivotal for various downstream tasks such as summarization, semantic case search and argument mining. Existing approaches often…
The Craft of Selective Prediction: Towards Reliable Case Outcome Classification -- An Empirical Study on European Court of Human Rights Cases
T. Y. S. S. Santosh, Irtiza Chowdhury, Shanshan Xu +1
In high-stakes decision-making tasks within legal NLP, such as Case Outcome Classification (COC), quantifying a model's predictive confidence is crucial. Confidence estimation enab…
Incorporating Precedents for Legal Judgement Prediction on European Court of Human Rights Cases
T. Y. S. S. Santosh, Mohamed Hesham Elganayni, Stanisław Sójka +1
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 fac…
Attack on Unfair ToS Clause Detection: A Case Study using Universal Adversarial Triggers
Shanshan Xu, Irina Broda, Rashid Haddad +2
Recent work has demonstrated that natural language processing techniques can support consumer protection by automatically detecting unfair clauses in the Terms of Service (ToS) Agr…
Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts
T. Y. S. S Santosh, Shanshan Xu, Oana Ichim +1
This work demonstrates that Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals that arise from corpus…
Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance
Abhishek Agarwal, Shanshan Xu, Matthias Grabmair
Summarizing legal decisions requires the expertise of law practitioners, which is both time- and cost-intensive. This paper presents techniques for extractive summarization of lega…