no code implementations • 23 May 2024 • T. Y. S. S Santosh, Tuan-Quang Vuong, Matthias Grabmair
This study investigates the challenges posed by the dynamic nature of legal multi-label text classification tasks, where legal concepts evolve over time.
no code implementations • 31 Mar 2024 • T. Y. S. S Santosh, Kristina Kaiser, Matthias Grabmair
In this paper, we introduce CuSINeS, a negative sampling approach to enhance the performance of Statutory Article Retrieval (SAR).
no code implementations • 31 Mar 2024 • T. Y. S. S Santosh, Hassan Sarwat, Ahmed Abdou, Matthias Grabmair
Rhetorical Role Labeling (RRL) of legal judgments is essential for various tasks, such as case summarization, semantic search and argument mining.
1 code implementation • 31 Mar 2024 • T. Y. S. S Santosh, Rashid Gustav Haddad, Matthias Grabmair
In common law jurisdictions, legal practitioners rely on precedents to construct arguments, in line with the doctrine of \emph{stare decisis}.
no code implementations • 31 Mar 2024 • T. Y. S. S Santosh, Elvin Quero Hernandez, Matthias Grabmair
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.
no code implementations • 31 Mar 2024 • T. Y. S. S Santosh, Mahmoud Aly, Matthias Grabmair
Legal professionals frequently encounter long legal judgments that hold critical insights for their work.
no code implementations • 28 Mar 2024 • T. Y. S. S Santosh, Vatsal Venkatkrishna, Saptarshi Ghosh, Matthias Grabmair
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.
no code implementations • 26 Feb 2024 • Santosh T. Y. S. S, Nina Baumgartner, Matthias Stürmer, Matthias Grabmair, Joel Niklaus
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.
no code implementations • 11 Feb 2024 • Shanshan Xu, T. Y. S. S Santosh, Oana Ichim, Barbara Plank, Matthias Grabmair
We observe limited alignment with the judge vote distribution.
no code implementations • 18 Oct 2023 • Shanshan Xu, T. Y. S. S Santosh, Oana Ichim, Isabella Risini, Barbara Plank, Matthias Grabmair
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.
1 code implementation • 17 Oct 2023 • Shanshan Xu, Leon Staufer, T. Y. S. S Santosh, Oana Ichim, Corina Heri, Matthias Grabmair
Our results demonstrate the challenging nature of the task with lower prediction performance and limited agreement between models and experts.
no code implementations • 13 Feb 2023 • T. Y. S. S. Santosh, Philipp Bock, Matthias Grabmair
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.
no code implementations • 1 Feb 2023 • T. Y. S. S Santosh, Marcel Perez San Blas, Phillip Kemper, Matthias Grabmair
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.
no code implementations • 1 Feb 2023 • T. Y. S. S Santosh, Oana Ichim, Matthias Grabmair
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.
no code implementations • 28 Nov 2022 • Shanshan Xu, Irina Broda, Rashid Haddad, Marco Negrini, Matthias Grabmair
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.
1 code implementation • 25 Oct 2022 • T. Y. S. S Santosh, Shanshan Xu, Oana Ichim, Matthias Grabmair
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.
no code implementations • 22 Oct 2022 • Abhishek Agarwal, Shanshan Xu, Matthias Grabmair
Summarizing legal decisions requires the expertise of law practitioners, which is both time- and cost-intensive.
1 code implementation • 15 Dec 2021 • Jaromir Savelka, Hannes Westermann, Karim Benyekhlef, Charlotte S. Alexander, Jayla C. Grant, David Restrepo Amariles, Rajaa El Hamdani, Sébastien Meeùs, Michał Araszkiewicz, Kevin D. Ashley, Alexandra Ashley, Karl Branting, Mattia Falduti, Matthias Grabmair, Jakub Harašta, Tereza Novotná, Elizabeth Tippett, Shiwanni Johnson
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).
1 code implementation • 20 Jun 2021 • Zihan Huang, Charles Low, Mengqiu Teng, Hongyi Zhang, Daniel E. Ho, Mark S. Krass, Matthias Grabmair
Lawyers and judges spend a large amount of time researching the proper legal authority to cite while drafting decisions.
1 code implementation • ICLR Workshop LLD 2019 • Mihir Kale, Aditya Siddhant, Sreyashi Nag, Radhika Parik, Matthias Grabmair, Anthony Tomasic
Pre-trained word embeddings are the primary method for transfer learning in several Natural Language Processing (NLP) tasks.
no code implementations • 17 Mar 2019 • Bhavya Karki, Fan Hu, Nithin Haridas, Suhail Barot, Zihua Liu, Lucile Callebert, Matthias Grabmair, Anthony Tomasic
Each QA instance comprises a table of either kind, a natural language question, and a corresponding structured SQL query.
no code implementations • WS 2018 • Soumya Wadhwa, Varsha Embar, Matthias Grabmair, Eric Nyberg
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.
no code implementations • WS 2017 • Ravich, Abhilasha er, Thomas Manzini, Matthias Grabmair, Graham Neubig, Jonathan Francis, Eric Nyberg
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.