“A dataset of merger agreements with rich expert annotations, Merger Agreement Understanding Dataset (MAUD) v1 is a corpus of 47,000+ labels in 152 merger agreements that have been manually labeled under the supervision of experienced lawyers to identify 92 questions in each agreement used by the 2021 American Bar Association (ABA) Public Target Deal Points Study. MAUD is curated and maintained by The Atticus Project, Inc. to support NLP research and development in legal contract review.” • 47,000+ labels; • 152 contracts; • 92 quest

• Check out the code for replicating the results and the trained model here; Reading Comprehension Task; Extraction Task.

See also the paper – MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding. Steven H. Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dimitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks. January 2, 2023. “Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association’s 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.”