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DeepRhole: deep learning for rhetorical role labeling of sentences in legal case documents

Paheli Bhattacharya, Shounak Paul, Kripabandhu Ghosh, Saptarshi Ghosh, Adam Wyner Orcid Logo

Artificial Intelligence and Law, Volume: 31, Issue: 1, Pages: 53 - 90

Swansea University Author: Adam Wyner Orcid Logo

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Abstract

The task of rhetorical role labeling is to assign labels (such as Fact, Argument, Final Judgement, etc.) to sentences of a court case document. Rhetorical role labeling is an important problem in the field of Legal Analytics, since it can aid in various downstream tasks as well as enhances the reada...

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Published in: Artificial Intelligence and Law
ISSN: 0924-8463 1572-8382
Published: Springer Science and Business Media LLC 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa65652
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Abstract: The task of rhetorical role labeling is to assign labels (such as Fact, Argument, Final Judgement, etc.) to sentences of a court case document. Rhetorical role labeling is an important problem in the field of Legal Analytics, since it can aid in various downstream tasks as well as enhances the readability of lengthy case documents. The task is challenging as case documents are highly various in structure and the rhetorical labels are often subjective. Previous works for automatic rhetorical role identification (i) mainly used Conditional Random Fields over manually handcrafted features, and (ii) focused on certain law domains only (e.g., Immigration cases, Rent law), and a particular jurisdiction/country (e.g., US, Canada, India). In this work, we improve upon the prior works on rhetorical role identification by proposing novel Deep Learning models for automatically identifying rhetorical roles, which substantially outperform the prior methods. Additionally, we show the effectiveness of the proposed models over documents from five different law domains, and from two different jurisdictions—the Supreme Court of India and the Supreme Court of the UK. Through extensive experiments over different variations of the Deep Learning models, including Transformer models based on BERT and LegalBERT, we show the robustness of the methods for the task. We also perform an extensive inter-annotator study and analyse the agreement of the predictions of the proposed model with the annotations by domain experts. We find that some rhetorical labels are inherently hard/subjective and both law experts and neural models frequently get confused in predicting them correctly.
Keywords: Rhetorical role labeling; Legal document segmentation; Court case documents; Hierarchical BiLSTM; Hierarchical BiLSTM CRF; BERT; LegalBERT
College: Faculty of Science and Engineering
Funders: The research is partially supported by SERB, Government of India, through a project titled “NYAYA: A Legal Assistance System for Legal Experts and the Common Man in India” and the TCG Centres for Research and Education in Science and Technology (CREST) through a project titled “Smart Legal Consultant: AI-based Legal Analytics”. P. Bhattacharya is supported by a Fellowship from Tata Consultancy Services.
Issue: 1
Start Page: 53
End Page: 90