Embracing the power of linguistics to transform legal and RegTech
Reviewing a contract is a time-consuming procedure. A study by the Exigent Group in 2019 [1] revealed that "60-80% of all business-to-business transactions are governed by some form of written agreement, with a typical Fortune 1,000 company maintaining 20,000 to 40,000 active contracts at any given time." This represents a significant cost for many companies as contract review is currently a manual process by trained professionals. For small companies or individuals who may have limited resources, this may mean that they have little choice other than to sign contracts without access to such professional services.
As part of our longstanding collaboration with Stanford University, we started to look at how we could use natural language processing (NLP) to tackle such a problem in 2020. Through this collaboration, we came up with the idea of formulating contract review automation as a natural language inference (NLI) problem, a classic, fundamental problem in linguistics. Specifically, given a contract and a set of hypotheses (such as "Some obligations of the agreement may survive termination."), we classify whether each hypothesis is “entailed by”, "contradicting to” (false) or “not mentioned by” (neutral) the contract and identify evidence for the decision as spans in the contract (Figure 1). This would allow users to “test” incoming contracts against their policy (e.g., raise an alert on contracts that entail "Some obligations in the Agreement may survive termination."). The same idea could be used for other Legal and RegTech problems, such as testing requirements (hypotheses) against documents in due diligence.
In this blog, I’d like to share an overview of our work. To find out more about the technical details, please read our recent publication at the Findings of EMNLP 2021 [2].
