[2301.00876] MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding
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Computer Science > Computation and Language

arXiv:2301.00876 (cs)
[Submitted on 2 Jan 2023 (v1), last revised 24 Nov 2023 (this version, v3)]

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

Authors: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.
Comments: EMNLP 2023. 5 pages + appendix. Code and dataset are available at this https URL
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2301.00876 [cs.CL]
  (or arXiv:2301.00876v3 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2301.00876

Submission history

From: Steven Wang [view email]
[v1] Mon, 2 Jan 2023 21:08:27 UTC (1,249 KB)
[v2] Fri, 6 Jan 2023 19:10:54 UTC (1,242 KB)
[v3] Fri, 24 Nov 2023 14:24:01 UTC (1,673 KB)
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