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.
翻译:许多专门领域仍不受深层学习的影响,因为大型的标签数据集需要昂贵的专家说明员。我们通过引入合同谅解Atticus数据集(CUAD)来解决法律领域内的这一瓶颈问题,这是一个用于法律合同审查的新数据集。CUAD是由来自Atticus项目数十名法律专家创建的,由13 000多份说明组成。任务是强调一项合同中对于人类审查十分重要的突出部分。我们发现,变换模型具有新生的性能,但这种性能受到模型设计和培训数据集大小的强烈影响。尽管取得了这些有希望的结果,但仍有很大的改进余地。作为专家注解的唯一大型、专门的NLP基准之一,CUAD可以作为更广泛的NLP群体具有挑战性的研究基准。