  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.
