CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review
This addresses the bottleneck of expensive expert annotation in legal NLP, providing a challenging benchmark for the broader community, though it is incremental as it focuses on dataset creation.
The authors tackled the lack of large labeled datasets in specialized domains like legal contract review by introducing CUAD, an expert-annotated dataset with over 13,000 annotations, and found that Transformer models show nascent performance with room for improvement.
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.