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arXiv · 2301.00876

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
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Abstract

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.

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Steven H. Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dimitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks. 2023-01-02. MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding. https://arxiv.org/abs/2301.00876

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