Chen, E; Roche, N; Tseng, YH; Hernandez, W; Shangguan, J; Moore, A; (2023) Conversion of Legal Agreements into Smart Legal Contracts using NLP. In: ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023. (pp. pp. 1112-1118). Association for Computing Machinery (ACM): Austin, TX, USA.

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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.

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