Design of AI-Driven Automated Translation System for Business Contracts

The proliferation of cross-border business transactions has heightened the demand for accurate and efficient translation of legal documents, particularly business contracts. Traditional translation workflows, reliant on human linguists, face scalability constraints and high operational costs. This paper introduces an AI-driven automated translation system tailored for business contracts, integrating transformer-based neural machine translation (NMT), domain-specific knowledge graphs, and human-in-the-loop post-editing optimization. The system addresses critical challenges in legal translation-such as terminology consistency, semantic ambiguity, and clause structure preservation-through a hybrid architecture combining multilingual pretraining, contract-specific fine-tuning, and contextual disambiguation via a legal knowledge graph (LegalKG). Empirical evaluations on a proprietary dataset of 500,000 aligned contract segments demonstrate a 34% improvement in BLEU scores and a 50% reduction in post-editing time compared to commercial MT systems. Case studies in multinational M&A agreements further validate the system’s practical utility in real-world legal scenarios.