CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoring

TL;DR

This paper introduces CLAUSEREC, a framework for recommending legal contract clauses using a two-stage process involving relevance prediction and clause suggestion, leveraging BERT-based models to assist contract authoring.

Contribution

It presents the first dedicated clause recommendation framework for contracts, combining relevance prediction and clause generation with a novel BERT-based approach.

Findings

Pretrained BERT improves clause relevance prediction.

Generation-based methods effectively recommend clauses.

Analysis highlights strengths and limitations of different methods.

Abstract

Contracts are a common type of legal document that frequent in several day-to-day business workflows. However, there has been very limited NLP research in processing such documents, and even lesser in generating them. These contracts are made up of clauses, and the unique nature of these clauses calls for specific methods to understand and generate such documents. In this paper, we introduce the task of clause recommendation, asa first step to aid and accelerate the author-ing of contract documents. We propose a two-staged pipeline to first predict if a specific clause type is relevant to be added in a contract, and then recommend the top clauses for the given type based on the contract context. We pretrain BERT on an existing library of clauses with two additional tasks and use it for our prediction and recommendation. We experiment with classification methods and similarity-based heuristics for clause relevance prediction, and generation-based methods for clause recommendation, and evaluate the results from various methods on several clause types. We provide analyses on the results, and further outline the advantages and limitations of the various methods for this line of research.