Incorporating Precedents for Legal Judgement Prediction on European Court of Human Rights Cases
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913521168547840 |
|---|---|
| author | Santosh, T. Y. S. S. Elganayni, Mohamed Hesham Sójka, Stanisław Grabmair, Matthias |
| author_facet | Santosh, T. Y. S. S. Elganayni, Mohamed Hesham Sójka, Stanisław Grabmair, Matthias |
| contents | Inspired by the legal doctrine of stare decisis, which leverages precedents (prior cases) for informed decision-making, we explore methods to integrate them into LJP models. To facilitate precedent retrieval, we train a retriever with a fine-grained relevance signal based on the overlap ratio of alleged articles between cases. We investigate two strategies to integrate precedents: direct incorporation at inference via label interpolation based on case proximity and during training via a precedent fusion module using a stacked-cross attention model. We employ joint training of the retriever and LJP models to address latent space divergence between them. Our experiments on LJP tasks from the ECHR jurisdiction reveal that integrating precedents during training coupled with joint training of the retriever and LJP model, outperforms models without precedents or with precedents incorporated only at inference, particularly benefiting sparser articles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18644 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Incorporating Precedents for Legal Judgement Prediction on European Court of Human Rights Cases Santosh, T. Y. S. S. Elganayni, Mohamed Hesham Sójka, Stanisław Grabmair, Matthias Computation and Language Inspired by the legal doctrine of stare decisis, which leverages precedents (prior cases) for informed decision-making, we explore methods to integrate them into LJP models. To facilitate precedent retrieval, we train a retriever with a fine-grained relevance signal based on the overlap ratio of alleged articles between cases. We investigate two strategies to integrate precedents: direct incorporation at inference via label interpolation based on case proximity and during training via a precedent fusion module using a stacked-cross attention model. We employ joint training of the retriever and LJP models to address latent space divergence between them. Our experiments on LJP tasks from the ECHR jurisdiction reveal that integrating precedents during training coupled with joint training of the retriever and LJP model, outperforms models without precedents or with precedents incorporated only at inference, particularly benefiting sparser articles. |
| title | Incorporating Precedents for Legal Judgement Prediction on European Court of Human Rights Cases |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.18644 |