Conversion of Legal Agreements into Smart Legal Contracts using NLP

TL;DR

This paper presents an NLP-based pipeline that automates converting legal contracts into Smart Legal Contracts within the Accord Project framework, improving efficiency and accessibility for legal and technical users.

Contribution

It introduces a novel NLP pipeline for automating SLC creation, including NER and question answering models, with a web interface for practical deployment.

Findings

NER pipeline achieves 0.8 accuracy in detecting CiceroMark

Question Answering extracts one-third of Concerto variables

Web interface enables user-friendly SLC creation

Abstract

A Smart Legal Contract (SLC) is a specialized digital agreement comprising natural language and computable components. The Accord Project provides an open-source SLC framework containing three main modules: Cicero, Concerto, and Ergo. Currently, we need lawyers, programmers, and clients to work together with great effort to create a usable SLC using the Accord Project. This paper proposes a pipeline to automate the SLC creation process with several Natural Language Processing (NLP) models to convert law contracts to the Accord Project's Concerto model. After evaluating the proposed pipeline, we discovered that our NER pipeline accurately detects CiceroMark from Accord Project template text with an accuracy of 0.8. Additionally, our Question Answering method can extract one-third of the Concerto variables from the template text. We also delve into some limitations and possible future research for the proposed pipeline. Finally, we describe a web interface enabling users to build SLCs. This interface leverages the proposed pipeline to convert text documents to Smart Legal Contracts by using NLP models.