Law Smells Detection with Machine Learning

Abstract

While major advancements have been achieved in many fields utilizing artificial intelligence for a variety of tasks, some specialized areas remain difficult to tackle. The legal domain is one such area. It is often said that legal language is a dialect of English and one that requires a Law degree to be fluent in. In this work we examined parallels between software engineering and legal drafting to develop definitions for contract smells, quick indications for potential issues with legal contracts. We created an auto labelled dataset of these contracts smells using engineered prompts and demonstrated how even a small set of human labels can significantly improve auto labelling results with few shots techniques. We demonstrated using bi-directional deep learning models that these contract smells can indeed be successfully detected automatically with high accuracy after further fine tuning BERT as well as LegalBert. This work underscores the feasibility of applying advanced NLP techniques to automate aspects of legal contract review and provides a strong foundation to further develop models for this purpose.