A Genre-Based Quality Evaluation of Chinese-English Translation by Online Machine Translation Systems
Qiong Yang · CSMFL Publications eBooks · 2024
The past decades have witnessed remarkable progress in machine translation (MT) quality, sparking a heated debate within and beyond academia about whether human translators will be replaced by MT systems. This case study conducts a genre-based manual quality evaluation of Chinese-to-English translations produced by Google Translate, Baidu Translate, Sogou Translate, Youdao Translate, and human translators. The findings reveal that the quality of online machine translation output remains incomparable to that of human translation across expressive, informative, and vocative genres. Sogou Translate excels in expressive and informative genres but requires significant improvements in fidelity and comprehensibility for expressive genres, as well as in fidelity and genre function reproducibility for informative genres. For vocative genres, the quality of online machine translation varies across the four evaluation criteria and necessitates targeted enhancements. Notably, Google Translate, despite its reputation as a leading machine translation system, lags behind others in reproducing genre-specific functions and ensuring comprehensibility. The study suggests that machine translation developers should leverage advanced AI language technology to develop an AI-assisted MT training system capable of interpreting nuances for more sophisticated translations.