Neural Legal Judgment Prediction in English

Ilias Chalkidis, Ion Androutsopoulos, Nikolaos Aletras


Abstract
Legal judgment prediction is the task of automatically predicting the outcome of a court case, given a text describing the case’s facts. Previous work on using neural models for this task has focused on Chinese; only feature-based models (e.g., using bags of words and topics) have been considered in English. We release a new English legal judgment prediction dataset, containing cases from the European Court of Human Rights. We evaluate a broad variety of neural models on the new dataset, establishing strong baselines that surpass previous feature-based models in three tasks: (1) binary violation classification; (2) multi-label classification; (3) case importance prediction. We also explore if models are biased towards demographic information via data anonymization. As a side-product, we propose a hierarchical version of BERT, which bypasses BERT’s length limitation.
Anthology ID:
P19-1424
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4317–4323
Language:
URL:
https://aclanthology.org/P19-1424
DOI:
10.18653/v1/P19-1424
Bibkey:
Cite (ACL):
Ilias Chalkidis, Ion Androutsopoulos, and Nikolaos Aletras. 2019. Neural Legal Judgment Prediction in English. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4317–4323, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Neural Legal Judgment Prediction in English (Chalkidis et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1424.pdf
Video:
 https://vimeo.com/385201206
Data
ECHR