Semantic Segmentation of Legal Documents via Rhetorical Roles
Vijit Malik, Rishabh Sanjay, Shouvik Kumar Guha, Angshuman Hazarika, Shubham Kumar Nigam, Arnab Bhattacharya, Ashutosh Modi
Abstract
Legal documents are unstructured, use legal jargon, and have considerable length, making them difficult to process automatically via conventional text processing techniques. A legal document processing system would benefit substantially if the documents could be segmented into coherent information units. This paper proposes a new corpus of legal documents annotated (with the help of legal experts) with a set of 13 semantically coherent units labels (referred to as Rhetorical Roles), e.g., facts, arguments, statute, issue, precedent, ruling, and ratio. We perform a thorough analysis of the corpus and the annotations. For automatically segmenting the legal documents, we experiment with the task of rhetorical role prediction: given a document, predict the text segments corresponding to various roles. Using the created corpus, we experiment extensively with various deep learning-based baseline models for the task. Further, we develop a multitask learning (MTL) based deep model with document rhetorical role label shift as an auxiliary task for segmenting a legal document. The proposed model shows superior performance over the existing models. We also experiment with model performance in the case of domain transfer and model distillation techniques to see the model performance in limited data conditions.- Anthology ID:
- 2022.nllp-1.13
- Volume:
- Proceedings of the Natural Legal Language Processing Workshop 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Nikolaos Aletras, Ilias Chalkidis, Leslie Barrett, Cătălina Goanță, Daniel Preoțiuc-Pietro
- Venue:
- NLLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 153–171
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.nllp-1.13/
- DOI:
- 10.18653/v1/2022.nllp-1.13
- Cite (ACL):
- Vijit Malik, Rishabh Sanjay, Shouvik Kumar Guha, Angshuman Hazarika, Shubham Kumar Nigam, Arnab Bhattacharya, and Ashutosh Modi. 2022. Semantic Segmentation of Legal Documents via Rhetorical Roles. In Proceedings of the Natural Legal Language Processing Workshop 2022, pages 153–171, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Semantic Segmentation of Legal Documents via Rhetorical Roles (Malik et al., NLLP 2022)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.nllp-1.13.pdf