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

被引：0

作者：

Wang, Steven H. ^{[1
]}

Scardigli, Antoine ^{[1
]}

Tang, Leonard ^{[2
]}

Chen, Wei ^{[3
]}

Levkin, Dimitry ^{[3
]}

Chen, Anya ^{[4
]}

Ball, Spencer ^{[5
]}

Woodside, Thomas ^{[6
]}

Zhang, Oliver ^{[7
]}

Hendrycks, Dan ^{[8
]}

机构：

[1] Swiss Fed Inst Technol, Zurich, Switzerland

[2] Harvard Univ, Cambridge, MA USA

[3] Atticus Project, Dorchester, England

[4] Nueva Sch, San Mateo, CA USA

[5] Univ Wisconsin, Madison, WI USA

[6] Yale Univ, New Haven, CT USA

[7] Stanford Univ, Stanford, CA USA

[8] Univ Calif Berkeley, Berkeley, CA USA

来源：

2023 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2023) | 2023年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：16369 / 16382

页数：14

共 24 条

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