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

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8770–8776 November 7–11, 2021. c⃝2021 Association for Computational Linguistics 8770 CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoring Vinay Aggarwal Adobe Research vinayagg@adobe.com Aparna Garimella Adobe Research garimell@adobe.com Balaji Vasan Srinivasan Adobe Research balsrini@adobe.com Anandhavelu N Adobe Research anandvn@adobe.com Rajiv Jain Adobe Research rajijain@adobe.com Abstract Contracts are a common type of legal docu- ment that frequent in several day-to-day busi- ness workflows. However, there has been very limited NLP research in processing such doc- uments, 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 intro- duce the task of clause recommendation, as a 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 con- tract, 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 re- sults 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. 1 Introduction A contract is a legal document between at least two parties that outlines the terms and conditions of the parties to an agreement. Contracts are typically in textual format, thus providing a huge potential for NLP applications in the space of legal documents. However, unlike most natural language corpora that are typically used in NLP research, contract lan- guage is repetitive with high inter-sentence similar- ities and sentence matches (Simonson et al., 2019), calling for new methods specific to legal language to understand and generate contract documents. A contract is essentially made up of clauses, which are provisions to address specific terms of the agreement, and which form the legal essence of the contract. Drafting a contract involves select- ing an appropriate template (with skeletal set of clauses), and customizing it for the specific pur- pose, typically via adding, removing, or modifying the various clauses in it. Both these stages involve manual effort and domain knowledge, and hence can benefit from assistance from NLP methods that are trained on large collections of contract docu- ments. In this paper, we attempt to take the first step towards AI-assisted contract authoring, and introduce the task of clause recommendation, and propose a two-staged approach to solve it. There have been some recent works on item- based and content-based recommendations. Wang and Fu (2020) reformulated the next sentence pre- diction task in BERT (Devlin et al., 2019) as next purchase prediction task to make a collabora- tive …

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