BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understanding
This addresses the challenge of analyzing commercial agreements where access to large legal corpora is difficult due to confidentiality, offering a competitive advantage for commercial and academic applications.
The paper tackled the problem of improving NLP tasks in the legal domain by fine-tuning BERT on legal documents, showing that this approach provides valuable improvements, though no concrete numbers are provided in the abstract.
Fine-tuning language models, such as BERT, on domain specific corpora has proven to be valuable in domains like scientific papers and biomedical text. In this paper, we show that fine-tuning BERT on legal documents similarly provides valuable improvements on NLP tasks in the legal domain. Demonstrating this outcome is significant for analyzing commercial agreements, because obtaining large legal corpora is challenging due to their confidential nature. As such, we show that having access to large legal corpora is a competitive advantage for commercial applications, and academic research on analyzing contracts.