Optimizing Language Service Efficiency Through Machine Translation and Human Proofreading Collaboration in Cross-Cultural Communication Scenarios
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Abstract
To address the conflict between machine translation and human proofreading in cross-cultural communication, this paper integrates language-service quality assessment with collaborative optimization theory and establishes an MT-HC collaborative efficiency optimization framework. Machine translation has advantages in speed but suffers from unstable accuracy and insufficient cultural adaptation, while human proofreading ensures quality but is costly and inefficient. The study defines three application scenarios: business negotiation, academic exchange, and public service, and specifies accuracy, cultural-adaptation, and time constraints for each. A three-stage workflow of “machine translation pretranslation-intelligent error classification-proofreading priority ranking” is designed. The BERT model, with an error recognition F1-score of 0.92, and fuzzy analytic hierarchy process are integrated to construct a proofreading-priority matrix. Comparative experiments show that the optimized model reduces business translation cycles by 42%, lowers academic proofreading costs by 38%, and decreases cultural-adaptation error rates in public services by 51%. It increases effective translation volume per unit time by 2.3 times and improves efficiency in minor-language scenarios by at least 1.8 times.
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