Abstract
Hierarchical classification frameworks have been widely used to process long sequences, especially in the legal domain for predictions from long legal documents. But being black-box models they are unable to explain their predictions making them less reliable for practical applications, more so in the legal domain. In this work, we develop an extractive explanation algorithm for hierarchical frameworks for long sequences based on the sensitivity of the trained model to its input perturbations. We perturb using occlusion and develop Ob-HEx; an Occlusion-based Hierarchical Explanation-extractor. We adapt Ob-HEx to Hierarchical Transformer models trained on long Indian legal texts. And use Ob-HEx to analyze them and extract their explanations for the ILDC-Expert dataset, achieving a minimum gain of 1 point over the previous benchmark on most of our performance evaluation metrics.- Anthology ID:
- 2024.findings-naacl.76
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2024
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1192–1201
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-naacl.76/
- DOI:
- 10.18653/v1/2024.findings-naacl.76
- Cite (ACL):
- Nishchal Prasad, Taoufiq Dkaki, and Mohand Boughanem. 2024. Explanation Extraction from Hierarchical Classification Frameworks for Long Legal Documents. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1192–1201, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Explanation Extraction from Hierarchical Classification Frameworks for Long Legal Documents (Prasad et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-naacl.76.pdf