Contracts are the cornerstone of legal agreements in the industry. As essential legal documents, contracts are subject to negotiations, demanding extensive analysis and evaluation efforts. Although the emergence of machine learning has enabled assistance tools for text analysis tasks, the specificity and constraints of each business context remain obstacles for the automation of contracts evaluation. In this paper, we propose an AutoML based approach for automated negotiation assistance that uses expert annotated contracts and the business-specific knowledge of acceptance policies. Driven by policies rules, our approach generates a classification process composed of hierarchies of complementary classifiers, each being automatically prepared according, but not limited to, feature extraction, learning model and data granularity. Experiments conducted on real-world service contracts have yielded promising results.
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