Design and Implementation of German Legal Decision Corpora

Urchs, Stefanie; Mitrović, Jelena

doi:10.5220/0010187305150521

Cited by 11 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The same or similar task has also been studied with court cases in many other jurisdictions including France (S ¸ulea et al, 2017), Philippines (Virtucio et al, 2018), Turkey (Mumcuoglu et al, 2021), Thailand (Kowsrihawat et al, 2018), United Kingdom (Strickson and De La Iglesia, 2020), Germany (Urchs et al, 2021), and Switzerland (Niklaus et al, 2021). Apart from predicting court decisions, there is also work aiming to interpret (explain) the decisions of particular courts (Ye et al, 2018;Chalkidis et al, 2021c;Branting et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

et al. 2021

View full text Add to dashboard Cite

Law, interpretations of law, legal arguments, agreements, etc. are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.

show abstract

Section: Related Workmentioning

confidence: 99%

LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

et al. 2021

View full text Add to dashboard Cite

show abstract

“…There is a increasing proliferation of new corpora specific to legal domains such as [569], [419], [305], [733]. Most of these corpora are designed to facilitate a specific task.…”

Section: Datasets and Legal Language Modelsmentioning

confidence: 99%

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Wörmann¹,

Bogdoll²,

Bührle³

et al. 2022

Preprint

View full text Add to dashboard Cite

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.Acknowledgement: The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Climate Action within the project "KI Wissen -Entwicklung von Methoden für die Einbindung von Wissen in maschinelles Lernen". The authors would like to thank the consortium for the successful cooperation.

show abstract

“…This work focuses on English texts. Legal texts in other languages [23] are also worth exploring in the future study of Legal AM. [18] PSI2020 CA, AD 42 doc Niculae et al [15] NPC2017 CDCP CA, AD 731 rec, 3,800 set Park and Cardie [17] PC2018 CA 731 rec, 3,800 set Galassi et al [7] GLT2021 AD Walker et al [26] WCDL2011 VICP CA 30 doc Grabmair et al [8] GACS2015 AD Walker et al [27] WHNY2017 BVA CA 20doc, 5,674 set Walker et al [24] WFPR2018 CA 30doc, 8,149 set Walker et al [28] WPDL2019 CA, AD 50doc, 6,153 set Westermann et al [31] WSWA2019 AD Walker et al [29] WSW2020 CA, AD 75 doc, 623 set Xu et al [32] XSA2020 CanLII CA, AD 683 doc, 30,374 set Xu et al [34] XSA2021a CA, AD 1,148 doc, 127,330 set Xu et al [33] XSA2021b CA, AD 2,098 doc, 226,576 set [12] provided the initial study on computational argumentation in legal text.…”

Section: Creating Annotated Legal Corporamentioning

confidence: 99%

A Decade of Legal Argumentation Mining: Datasets and Approaches

Zhang

Nulty

Lillis

2022

Natural Language Processing and Information Systems

View full text Add to dashboard Cite

The growing research field of argumentation mining (AM) in the past ten years has made it a popular topic in Natural Language Processing. However, there are still limited studies focusing on AM in the context of legal text (Legal AM), despite the fact that legal text analysis more generally has received much attention as an interdisciplinary field of traditional humanities and data science. The goal of this work is to provide a critical data-driven analysis of the current situation in Legal AM. After outlining the background of this topic, we explore the availability of annotated datasets and the mechanisms by which these are created. This includes a discussion of how arguments and their relationships can be modelled, as well as a number of different approaches to divide the overall Legal AM task into constituent sub-tasks. Finally we review the dominant approaches that have been applied to this task in the past decade, and outline some future directions for Legal AM research.

show abstract

Design and Implementation of German Legal Decision Corpora

Cited by 11 publications

References 0 publications

LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

A Decade of Legal Argumentation Mining: Datasets and Approaches

Contact Info

Product

Resources

About