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

Steven H. Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dimitry Levkin +5 more
1/2/2023
cs.CL

No official code found. Best implementation: TheAtticusProject/maud (101 stars, last commit 2023-02-15). 1 more below.

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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Code Implementations(2)

TheAtticusProject/maudCommunity100%
10114Shell, PythonJan 2, 20233 years ago
TheAtticusProject/maud-extractionCommunity100%
147Shell, PythonJan 2, 20232 years ago

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Cite this paper

@article{wang2023maud,
  title  = {MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding},
  author = {Steven H. Wang and Antoine Scardigli and Leonard Tang and Wei Chen and Dimitry Levkin and Anya Chen and Spencer Ball and Thomas Woodside and Oliver Zhang and Dan Hendrycks},
  year   = {2023},
  eprint = {2301.00876},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.00876v3}
}

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