LegalEval

Authors:

• Yuri Noviello
• Enrico Pallotta
• Flavio Pinzarrone
• Giuseppe Tanzi

Project work for the "Natural Language Processing" course of the Artificial Intelligence master's degree at University of Bologna. This code in this repository is a devolopment of the first two tasks of the LegalEval challenge of SemEval 2023.

The LegalEval challenge proposes three tasks, based on Indian Legal documents:

• Rhetorical Roles prediction
• Legal Named Entity Recognition
• Court Judgement Prediction with Explanation.

Introduction

Our work focuses on the first two tasks. For the first task we present a context-aware approach to enhance sentence information. With the help of this approach, the classification model utilizing InLegalBert as a transformer achieved 81.12% Micro-F1. For the second task we present a NER approach to extract and classify entities like names of petitioner, respondent, court or statute from a given document. The model utilizing XLNet as transformer and a dependency parser on top achieved 87.43% Macro-F1.

Task A

The objective of the task is to segment a given legal document by predicting the rhetorical role label for each sentence such as a preamble, fact, ratio, arguments, etc. These are referred to as Rhetorical Roles (RR). This segmentation is a fundamental building block for many legal AI applications like judgment summarizing, judgment outcome prediction, precedent search, etc.

Best model architecture

Context aware InLegalBERT

Output Example

Example of a segmented document

Results

		Validation Set			Test Set
Models	Weighted Precision	Weighted Recall	Micro F1	Weighted Precision	Weighted Recall	Micro F1
Context Aware Legal-RoBERTa	77.0	76.0	76.0	79.0	80.0	80.0
Context Aware InLegalBERT	77.0	77.0	78.0	81.0	82.0	82.0

Task B

The objective of the task is to extract legal named entities from court judgment texts to effectively generate metadata information that can be exploited for many legal applications like knowledge graph creation, co-reference resolution and in general to build any query-able knowledge base that would allow faster information access.

Best model architecture

Transformer + BiLSTM + CRF

Output Example

Example of Legal Named Entities detected in a sentence

Results

		Validation Set			Test Set
Models	Macro Precision	Macro Recall	Macro F1	Macro Precision	Macro Recall	Macro F1
RoBERTa - BiLSTM - CRF	76.0	82.3	79.0	85.5	88.4	87.0
XLNet - BiLSTM - CRF	85.3	86.8	84.0	85.9	90.4	88.1

References

• LegalEval challenge leaderboard