Automated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language

Mohr Wenger, Tom Kalir, Noga Berger, Carmit Klar Chalamish, Renana Keydar, Gabriel Stanovsky


Abstract
We present the task of Automated Punishment Extraction (APE) in sentencing decisions from criminal court cases in Hebrew. Addressing APE will enable the identification of sentencing patterns and constitute an important stepping stone for many follow up legal NLP applications in Hebrew, including the prediction of sentencing decisions. We curate a dataset of sexual assault sentencing decisions and a manually-annotated evaluation dataset, and implement rule-based and supervised models. We find that while supervised models can identify the sentence containing the punishment with good accuracy, rule-based approaches outperform them on the full APE task. We conclude by presenting a first analysis of sentencing patterns in our dataset and analyze common models’ errors, indicating avenues for future work, such as distinguishing between probation and actual imprisonment punishment. We will make all our resources available upon request, including data, annotation, and first benchmark models.
Anthology ID:
2021.nllp-1.4
Volume:
Proceedings of the Natural Legal Language Processing Workshop 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Editors:
Nikolaos Aletras, Ion Androutsopoulos, Leslie Barrett, Catalina Goanta, Daniel Preotiuc-Pietro
Venue:
NLLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
36–45
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2021.nllp-1.4/
DOI:
10.18653/v1/2021.nllp-1.4
Bibkey:
Cite (ACL):
Mohr Wenger, Tom Kalir, Noga Berger, Carmit Klar Chalamish, Renana Keydar, and Gabriel Stanovsky. 2021. Automated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language. In Proceedings of the Natural Legal Language Processing Workshop 2021, pages 36–45, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Automated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language (Wenger et al., NLLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2021.nllp-1.4.pdf