Investigating Strategies for Clause Recommendation

TL;DR

This paper explores methods for recommending legal clauses by leveraging representations of similar contracts, aiming to improve clause generation for various common clause types in legal documents.

Contribution

It introduces an approach that utilizes representations of similar contracts to enhance clause recommendation, expanding prior work to cover 15 clause types and analyzing different settings.

Findings

Representation of similar contracts improves clause recommendation accuracy.

Analyzed the impact of different settings on clause recommendation performance.

Extended previous methods to 15 clause types in legal contracts.

Abstract

Clause recommendation is the problem of recommending a clause to a legal contract, given the context of the contract in question and the clause type to which the clause should belong. With not much prior work being done toward the generation of legal contracts, this problem was proposed as a first step toward the bigger problem of contract generation. As an open-ended text generation problem, the distinguishing characteristics of this problem lie in the nature of legal language as a sublanguage and the considerable similarity of textual content within the clauses of a specific type. This similarity aspect in legal clauses drives us to investigate the importance of similar contracts' representation for recommending clauses. In our work, we experiment with generating clauses for 15 commonly occurring clause types in contracts expanding upon the previous work on this problem and analyzing clause recommendations in varying settings using information derived from similar contracts.