Claim Extraction and Law Matching for COVID-19-related Legislation

Niklas Dehio, Malte Ostendorff, Georg Rehm

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.
Anthology ID:
2022.lrec-1.50
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
480–490
Language:
URL:
https://aclanthology.org/2022.lrec-1.50
DOI:
Bibkey:
Cite (ACL):
Niklas Dehio, Malte Ostendorff, and Georg Rehm. 2022. Claim Extraction and Law Matching for COVID-19-related Legislation. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 480–490, Marseille, France. European Language Resources Association.
Cite (Informal):
Claim Extraction and Law Matching for COVID-19-related Legislation (Dehio et al., LREC 2022)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.50.pdf
Code
 dfki-nlp/covid19-law-matching
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