Chalkidis, I., Androutsopoulos, I. and Aletras, N. (2019) Neural legal judgment prediction in English. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 57th Annual Meeting of the Association for Computational Linguistics, 28 Jul - 02 Aug 2019, Florence, Italy. ACL , pp. 4317-4323.
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 2019 Association for Computational Linguistics. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Computation and Language |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 31 Jul 2019 13:20 |
Last Modified: | 09 Jan 2020 15:15 |
Status: | Published |
Publisher: | ACL |
Refereed: | Yes |
Identification Number: | 10.18653/v1/P19-1424 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149064 |