Abstract
Statutory reasoning is the task of determining whether a given law – a part of a statute – applies to a given legal case. Previous work has shown that structured, logical representations of laws and cases can be leveraged to solve statutory reasoning, including on the StAtutory Reasoning Assessment dataset (SARA), but rely on costly human translation into structured representations. Here, we investigate a form of legal information extraction atop the SARA cases, illustrating how the task can be done with high performance. Further, we show how the performance of downstream symbolic reasoning directly correlates with the quality of the information extraction.- Anthology ID:
- 2023.nllp-1.12
- Volume:
- Proceedings of the Natural Legal Language Processing Workshop 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Daniel Preoțiuc-Pietro, Catalina Goanta, Ilias Chalkidis, Leslie Barrett, Gerasimos Spanakis, Nikolaos Aletras
- Venues:
- NLLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 113–131
- Language:
- URL:
- https://aclanthology.org/2023.nllp-1.12
- DOI:
- 10.18653/v1/2023.nllp-1.12
- Cite (ACL):
- Nils Holzenberger and Benjamin Van Durme. 2023. Connecting Symbolic Statutory Reasoning with Legal Information Extraction. In Proceedings of the Natural Legal Language Processing Workshop 2023, pages 113–131, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Connecting Symbolic Statutory Reasoning with Legal Information Extraction (Holzenberger & Van Durme, NLLP-WS 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.nllp-1.12.pdf