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
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.findings-emnlp.361.pdf
- Code
- jsavelka/statutory_interpretation
- Data
- Statutory Interpretation Data Set