A Benchmark for Lease Contract Review
Spyretta Leivaditi University of Amsterdam \AndJulien Rossi University of Amsterdam \AndEvangelos Kanoulas University of Amsterdam
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 research111Our codebase and model weights a
中文速览
租约合同（lease agreement）中藏着大量法律风险，但人工逐条审查既费时又昂贵，亟需自动化工具的辅助。研究团队针对这一痛点，定义了14类合同实体（如出租方信息、租金条款、起止日期等）和17类"红旗"风险条款（如提前解约选项、转租权、优先购买权等），并从美国证券交易委员会的公开数据库中筛选出179份租约文件进行人工标注，构建了首个面向租约审查的基准数据集。在此基础上，他们在该语料上持续预训练了一个专用语言模型ALeaseBERT，并针对实体抽取和红旗检测两项任务进行微调，在实体识别上取得了较高的F1分数，红旗检测因样本稀少而难度更大但也给出了可参考的基线结果。这项工作填补了租约合同智能审查领域缺乏标注数据和专用模型的空白，为法律科技（LegalTech）领域后续研究提供了可复用的数据集与模型基线。
原文 arXiv:2010.10386;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2010.10386v1