maud_cor_standard_(superior_offer)

Read an excerpt from a merger agreement and answer: what standard should the board follow when determining whether to change its recommendation in connection with a superior offer?

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(zehuali@stanford.edu)

Source: Atticus Project

License: CC By 4.0

Size (samples): 101

Legal reasoning type: Interpretation

Task type: 10-way classification

Task description

This is a multiple-choice task in which the model must select the answer that best characterizes the merger agreement.

Task construction

This task was constructed from the MAUD dataset, which consists of over 47,000 labels across 152 merger agreements annotated to identify 92 questions in each agreement used by the 2021 American Bar Association (ABA) Public Target Deal Points Study. The task is formatted as a series of multiple-choice questions, where given a segment of the merger agreement and a Deal Point question, the model is to choose the answer that best characterizes the agreement as response.

Question: Read an excerpt from a merger agreement and answer: what standard should the board follow when determining whether to change its recommendation in connection with a superior offer?

Options:
A: "Breach" of fiduciary duties
B: "Inconsistent" with fiduciary duties
C: "Reasonably likely/expected breach" of fiduciary duties
D: "Reasonably likely/expected to be inconsistent" with fiduciary duties
E: "Reasonably likely/expected violation" of fiduciary duties
F: "Required to comply" with fiduciary duties
G: "Violation" of fiduciary duties
H: More likely than not violate fiduciary duties
I: None
J: Other specified standard

Citation information

If you use this dataset, we ask that you also cite to the source of the data as well.

@article{wang2023maud,
  title={MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding},
  author={Wang, Steven H and Scardigli, Antoine and Tang, Leonard and Chen, Wei and Levkin, Dimitry and Chen, Anya and Ball, Spencer and Woodside, Thomas and Zhang, Oliver and Hendrycks, Dan},
  journal={arXiv preprint arXiv:2301.00876},
  year={2023}
}