A Deep Learning Approach for Norm Conflict Identification

Contracts represent agreements between two or more parties formally in the form of deontic statements or norms within their clauses. If not carefully designed, such conflicts may invalidate an entire contract, and thus great effort is made to write conflict-free contracts using human reviewers that, when applied to complex and long contracts, can be time consuming and error-prone. In this work, we develop an approach to automate the identification of potential conflicts between norms in contracts. We build a two-phase approach that uses traditional machine learning together with deep learning to extract and compare norms in order to identify conflicts between them. Using a manually annotated set of conflicts as train and test set, our approach reaches over 90% accuracy, establishing a new state-of-the art.