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

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.

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“…In recent years, a few legal corpora have been proposed in English, such as LEDGAR [18], CUAD [10], BillSum [19], MAUD [11], and EUR-Lex-Sum [20]. The first, LEDGAR, consists of 100,000 provisions to be classified as provisions types (e.g.…”

“…Thus, creating curated large legal annotated corpora has been proven to be costly [10,11]. For example, MAUD, an expert-annotated merger agreement understanding dataset, has been estimated to cost $5 million using the standard hourly fees of specialized lawyers [11].…”

“…Thus, creating curated large legal annotated corpora has been proven to be costly [10,11]. For example, MAUD, an expert-annotated merger agreement understanding dataset, has been estimated to cost $5 million using the standard hourly fees of specialized lawyers [11]. Despite the challenges, there has understandably been great interest in exploring the possibility of deep learning techniques such as the use of Transformer architecture (i.e.…”