Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models

Jaromir Savelka, Kevin Ashley


Abstract
Legal texts routinely use concepts that are difficult to understand. Lawyers elaborate on the meaning of such concepts by, among other things, carefully investigating how they have been used in the past. Finding text snippets that mention a particular concept in a useful way is tedious, time-consuming, and hence expensive. We assembled a data set of 26,959 sentences, coming from legal case decisions, and labeled them in terms of their usefulness for explaining selected legal concepts. Using the dataset we study the effectiveness of transformer models pre-trained on large language corpora to detect which of the sentences are useful. In light of models’ predictions, we analyze various linguistic properties of the explanatory sentences as well as their relationship to the legal concept that needs to be explained. We show that the transformer-based models are capable of learning surprisingly sophisticated features and outperform the prior approaches to the task.
Anthology ID:
2021.findings-emnlp.361
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4273–4283
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.361
DOI:
10.18653/v1/2021.findings-emnlp.361
Bibkey:
Cite (ACL):
Jaromir Savelka and Kevin Ashley. 2021. Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4273–4283, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models (Savelka & Ashley, Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.361.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.361.mp4
Code
 jsavelka/statutory_interpretation
Data
Statutory Interpretation Data Set