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MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding

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arxiv 2301.00876 v3 pith:Q5RUHLOH submitted 2023-01-02 cs.CL

Steven H. Wang , Antoine Scardigli , Leonard Tang , Wei Chen , Dimitry Levkin , Anya Chen , Spencer Ball , Thomas Woodside
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Oliver Zhang Dan Hendrycks
This is my paper
classification cs.CL
keywords datasetexpert-annotatedlegalagreementmaudmergercomprehensionquestions
verification ladder T0 review T1 audit T2 compute T3 formal
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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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