A Benchmark for Lease Contract Review

TL;DR

This paper introduces a new benchmark dataset and a specialized language model for extracting entities and red flags from lease agreements to improve legal contract review automation.

Contribution

It provides the first annotated dataset for lease contracts and a pre-trained language model tailored for entity and red flag detection in this domain.

Findings

Created a dataset of 179 annotated lease agreements

Developed ALeaseBERT, a domain-specific language model

Established baseline results for entity and red flag extraction

Abstract

Extracting entities and other useful information from legal contracts is an important task whose automation can help legal professionals perform contract reviews more efficiently and reduce relevant risks. In this paper, we tackle the problem of detecting two different types of elements that play an important role in a contract review, namely entities and red flags. The latter are terms or sentences that indicate that there is some danger or other potentially problematic situation for one or more of the signing parties. We focus on supporting the review of lease agreements, a contract type that has received little attention in the legal information extraction literature, and we define the types of entities and red flags needed for that task. We release a new benchmark dataset of 179 lease agreement documents that we have manually annotated with the entities and red flags they contain, and which can be used to train and test relevant extraction algorithms. Finally, we release a new language model, called ALeaseBERT, pre-trained on this dataset and fine-tuned for the detection of the aforementioned elements, providing a baseline for further research