MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding
This provides a benchmark for the legal profession and NLP community to improve understanding of merger agreements, though it is incremental as it focuses on a specific domain.
The authors tackled the challenge of reading comprehension in legal text by introducing MAUD, an expert-annotated dataset with over 39,000 examples, and their fine-tuned Transformer baselines performed well above random on most questions.
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.