CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review

TL;DR

CUAD is a large, expert-annotated NLP dataset for legal contract review, designed to facilitate research in legal NLP by providing a challenging benchmark with over 13,000 annotations.

Contribution

The paper introduces CUAD, a new expert-annotated legal NLP dataset with over 13,000 annotations, addressing the lack of large, specialized datasets in legal NLP.

Findings

Transformer models show nascent performance on CUAD

Model performance is heavily influenced by design and dataset size

There is significant room for improvement in legal contract understanding

Abstract

Many specialized domains remain untouched by deep learning, as large labeled datasets require expensive expert annotators. We address this bottleneck within the legal domain by introducing the Contract Understanding Atticus Dataset (CUAD), a new dataset for legal contract review. CUAD was created with dozens of legal experts from The Atticus Project and consists of over 13,000 annotations. The task is to highlight salient portions of a contract that are important for a human to review. We find that Transformer models have nascent performance, but that this performance is strongly influenced by model design and training dataset size. Despite these promising results, there is still substantial room for improvement. As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community.