Multilingual Machine Translation Quality Estimation Framework

This technical whitepaper presents the Pangeanic MTQE v2, a multilingual machine translation quality estimation system designed for production environments. The document outlines the operational challenges faced by organizations in trusting and deploying machine-generated translations at scale. It emphasizes the need for a specialized quality layer that evaluates every source-translation pair before publication, rather than solely relying on general-purpose models. The MTQE v2 system converts translation evidence into operational decisions, determining whether to accept, repair, or escalate translations for human review. The whitepaper details the benchmark methodology used to evaluate the performance of MTQE v2 across multiple languages and scenarios, highlighting its ability to operate in a reference-free setting. It also discusses the evaluation workflow, which includes generating quality scores and natural-language explanations for detected issues. The implications for decision-makers are addressed, focusing on the shift from universal human inspection to governed exception handling, allowing organizations to maintain control over translation quality while improving efficiency.