Factoring Statutory Reasoning as Language Understanding Challenges

Nils Holzenberger, Benjamin Van Durme


Abstract
Statutory reasoning is the task of determining whether a legal statute, stated in natural language, applies to the text description of a case. Prior work introduced a resource that approached statutory reasoning as a monolithic textual entailment problem, with neural baselines performing nearly at-chance. To address this challenge, we decompose statutory reasoning into four types of language-understanding challenge problems, through the introduction of concepts and structure found in Prolog programs. Augmenting an existing benchmark, we provide annotations for the four tasks, and baselines for three of them. Models for statutory reasoning are shown to benefit from the additional structure, improving on prior baselines. Further, the decomposition into subtasks facilitates finer-grained model diagnostics and clearer incremental progress.
Anthology ID:
2021.acl-long.213
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2742–2758
Language:
URL:
https://aclanthology.org/2021.acl-long.213
DOI:
10.18653/v1/2021.acl-long.213
Bibkey:
Cite (ACL):
Nils Holzenberger and Benjamin Van Durme. 2021. Factoring Statutory Reasoning as Language Understanding Challenges. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2742–2758, Online. Association for Computational Linguistics.
Cite (Informal):
Factoring Statutory Reasoning as Language Understanding Challenges (Holzenberger & Van Durme, ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/nodalida-main-page/2021.acl-long.213.pdf
Video:
 https://preview.aclanthology.org/nodalida-main-page/2021.acl-long.213.mp4
Code
 SgfdDttt/sara_v2
Data
SARA