Complex Labelling and Similarity Prediction in Legal Texts: Automatic Analysis of France’s Court of Cassation Rulings
14846Authors
nan
Abstract
AbstractDetecting divergences in the applications of the law (where the same legal text is applied differently by two rulings) is an important task. It is the mission of the French Cour de Cassation. The first step in the detection of divergences is to detect similar cases, which is currently done manually by experts. They rely on summarised versions of the rulings (syntheses and keyword sequences), which are currently produced manually and are not available for all rulings. There is also a high degree of variability in the keyword choices and the level of granularity used. In this article, we therefore aim to provide automatic tools to facilitate the search for similar rulings. We do this by (i) providing automatic keyword sequence generation models, which can be used to improve the coverage of the analysis, and (ii) providing measures of similarity based on the available texts and augmented with predicted keyword sequences. Our experiments show that the predictions improve correlations of automatically obtained similarities against our specially colelcted human judgments of similarity.
Keywords
Full Content
Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 480–490 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 480 Claim Extraction and Law Matching for COVID-19-related Legislation Niklas Dehio, Malte Ostendorff, Georg Rehm DFKI GmbH, Alt-Moabit 91c, 10559 Berlin, Germany niklas.dehio@gmail.com, {malte.ostendorff, georg.rehm}@dfki.de Abstract To cope with the COVID-19 pandemic, many jurisdictions have introduced new or altered existing legislation. Even though these new rules are often communicated to the public in news articles, it remains challenging for laypersons to learn about what is currently allowed or forbidden since news articles typically do not reference underlying laws. We investigate an automated approach to extract legal claims from news articles and to match the claims with their corresponding applicable laws. We examine the feasibility of the two tasks concerning claims about COVID-19-related laws from Berlin, Germany. For both tasks, we create and make publicly available the data sets and report the results of initial experiments. We obtain promising results with Transformer-based models that achieve 46.7 F1 for claim extraction and 91.4 F1 for law matching, albeit with some conceptual limitations. Furthermore, we discuss challenges of current machine learning approaches for legal language processing and their ability for complex legal reasoning tasks. Keywords: Natural Legal Language Processing, Text Matching, Claim Extraction, Legal Reasoning, COVID-19 1. Introduction The COVID-19 pandemic has led to a flurry of activity by law-making bodies in many jurisdictions around the world, for instance in Germany (Siewert et al., 2020). The rapid spread of the contagious coronavirus since December 2019 demanded both the necessity to pass laws that deeply intervene into our constitutional rights and to do so in a timely manner. However, the un- predictable nature of the pandemic lead to a situation where those laws were altered again and again, often on short notice, based on new insights produced by the scientific process. While new rules were often communicated to the pub- lic via newspapers or public statements, these often did not provide a reference to the actual law itself. Find- ing the corresponding law is a non-trivial task in itself, also due to the technical language used in laws – also called “legalese” (Mar´ın, 2017). In addition, under- standing the applicability of laws requires specific rea- soning skills that legal professionals like lawyers are trained in, but laypersons usually are not. Having an automated system that finds the referenced laws would be a valuable contribution to counter disinformation and enable citizens to look up legal rules themselves. In this paper, we discuss the challenges of legal rea- soning in the context of machine learning and exam- ine the feasibility of two tasks. First, the claim extrac- tion task, where claims about COVID-19-related legis- lation are extracted from newspaper articles. Second, the law matching task, where such claims are matched with relevant subsections from the corresponding laws. For both tasks, we annotate and make available data sets based on German news articles and German leg- islation.1 Moreover, we report …
Content truncated to 500 words
Labeled Text
AbstractDetecting divergences in the applications of the law (where the same legal text is applied differently by two rulings) is an important task. It is the mission of the French Cour de Cassation. The first step in the detection of divergences is to detect similar cases, which is currently done manually by experts. They rely on summarised versions of the rulings (syntheses and keyword sequences), which are currently produced manually and are not available for all rulings. There is also a high degree of variability in the keyword choices and the level of granularity used. In this article, we therefore aim to provide automatic tools to facilitate the search for similar rulings. We do this by (i) providing automatic keyword sequence generation models, which can be used to improve the coverage of the analysis, and (ii) providing measures of similarity based on the available texts and augmented with predicted keyword sequences. Our experiments show that the predictions improve correlations of automatically obtained similarities against our specially colelcted human judgments of similarity.
Publication Details
- Paper ID:
- 14846
- Year:
- 2022
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan