Law Smells Detection with Machine Learning
Citation
Dechtiar, Moriya. 2024. Law Smells Detection with Machine Learning. Master's thesis, Harvard University Division of Continuing Education.Abstract
While major advancements have been achieved in many fields utilizing artificialintelligence 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.
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https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37378564
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