MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding
Steven Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dmitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks
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.- Anthology ID:
- 2023.emnlp-main.1019
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16369–16382
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.1019
- DOI:
- 10.18653/v1/2023.emnlp-main.1019
- Cite (ACL):
- Steven Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dmitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, and Dan Hendrycks. 2023. MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16369–16382, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding (Wang et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.emnlp-main.1019.pdf