Hybrid Legal Clause Classification: Integrating Supervised BERT with Reinforcement Learning

Detecting ambiguity or classifying as clear or vague is a crucial yet important task in legal document analysis, which often requires human feedback. This research involves a unique approach by integrating supervised Machine Learning with Reinforcement Learning for automatic legal clause classification. This work proposes a goal to develop a system which is able to accurately identify ambiguity in legal documents by pre-trained BERT embed dings and dynamic learning methods. The initial part of the system is trained using labeled data that predicts the clause as ambiguous or non-ambiguous. This model uses BERT for clause representation. The latter part of the system inculcates Reinforcement Learning (RL) algorithm such as Q-Learning to interact with the environment created by legal dataset. The agent used in implementation of Reinforcement learning learns to classify clauses by receiving rewards as feedback. After multiple training episodes of RL agent, it dynamically improves its decision making by maximizing cumulative reward. This integrated system of Supervised Machine Learning and Reinforcement Learning helps the system to handle situations with well-labeled datasets and environments with available feedback but limited direct supervision. The proposed model showcases the potential of the integrated system for legal document analysis and scalable solution for clause classification task.