Comparative Evaluation of AI Translation Models for Mandarin Business Communication

This study benchmarks the semantic accuracy of three major AI translators - ChatGPT, Google Translate, and Microsoft Copilot - for English-to-Mandarin business communication. Using 152 professionally translated sentence pairs, we evaluate the semantic alignment between machine translations and human references using three embedding models: Sentence-BERT (SBERT), Universal Sentence Encoder (USE), and XLM-RoBERTa (XLM-R). Results show that Microsoft Copilot consistently outperforms the other systems in semantic fidelity, while XLM-R exhibits a near-saturation effect, suggesting potential metric bias in cross-lingual similarity scoring. To contextualize these embedding-based findings, BLEU and METEOR baselines are included, alongside statistical significance testing using ANOVA and paired t-tests. The study contributes a reproducible evaluation framework for embedding-based translation assessment and provides practical insights for selecting reliable AI translation tools in business communication. Future work includes expanding the dataset, incorporating additional business domains, and integrating human expert evaluations to complement automated semantic metrics.