Publication
Claim Extraction and Law Matching for COVID-19-related Legislation
Niklas Dehio; Malte Ostendorff; Georg Rehm
In: Proceedings of the 13th Language Resources and Evaluation Conference. International Conference on Language Resources and Evaluation (LREC-2022), European Language Resources Association (ELRA), 2022.
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.
Projects
- QURATOR - Flexible AI Technologies for the Adaptive Analysis and Creative Generation of Digital Content in Various Contexts
- PANQURA - KI-Technologien für die personalisierte Kuratierung von Online-Beiträgen zur Gewährleistung von Informationstransparenz in Krise