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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.09356 |
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| _version_ | 1866912053624569856 |
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| author | Mahari, Robert Stammbach, Dominik Ash, Elliott Pentland, Alex `Sandy' |
| author_facet | Mahari, Robert Stammbach, Dominik Ash, Elliott Pentland, Alex `Sandy' |
| contents | We present the Legal Passage Retrieval Dataset LePaRD. LePaRD is a massive collection of U.S. federal judicial citations to precedent in context. The dataset aims to facilitate work on legal passage prediction, a challenging practice-oriented legal retrieval and reasoning task. Legal passage prediction seeks to predict relevant passages from precedential court decisions given the context of a legal argument. We extensively evaluate various retrieval approaches on LePaRD, and find that classification appears to work best. However, we note that legal precedent prediction is a difficult task, and there remains significant room for improvement. We hope that by publishing LePaRD, we will encourage others to engage with a legal NLP task that promises to help expand access to justice by reducing the burden associated with legal research. A subset of the LePaRD dataset is freely available and the whole dataset will be released upon publication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_09356 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LePaRD: A Large-Scale Dataset of Judges Citing Precedents Mahari, Robert Stammbach, Dominik Ash, Elliott Pentland, Alex `Sandy' Computation and Language We present the Legal Passage Retrieval Dataset LePaRD. LePaRD is a massive collection of U.S. federal judicial citations to precedent in context. The dataset aims to facilitate work on legal passage prediction, a challenging practice-oriented legal retrieval and reasoning task. Legal passage prediction seeks to predict relevant passages from precedential court decisions given the context of a legal argument. We extensively evaluate various retrieval approaches on LePaRD, and find that classification appears to work best. However, we note that legal precedent prediction is a difficult task, and there remains significant room for improvement. We hope that by publishing LePaRD, we will encourage others to engage with a legal NLP task that promises to help expand access to justice by reducing the burden associated with legal research. A subset of the LePaRD dataset is freely available and the whole dataset will be released upon publication. |
| title | LePaRD: A Large-Scale Dataset of Judges Citing Precedents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.09356 |