BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understanding

TL;DR

Fine-tuning BERT on large legal corpora significantly enhances NLP performance in legal contract analysis, providing a competitive edge for commercial and academic applications despite data confidentiality challenges.

Contribution

This paper demonstrates the benefits of domain-specific fine-tuning of BERT on legal texts and highlights the importance of access to large legal corpora for improved NLP tasks.

Findings

Legal BERT fine-tuning improves contract understanding.

Access to large legal corpora offers a competitive advantage.

Legal NLP tasks benefit from domain-specific language models.

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.