Yanli Zhang*, Cuiyun Tang, Ying Guo
Tangshan Normal University, Tangshan 063000, Hebei, China.
*Corresponding author: Yanli Zhang
Funding: Project supported by the 2025 University-level Research Project (Discipline Development) of Tangshan Normal University: AI-Assisted English Translation of Classic Plays of Pingju Opera (Project No. 20251123023).
Abstract
With the advancement of artificial intelligence (AI), large language models (LLMs) have demonstrated broad application prospects in the field of subtitle translation. This study takes two representative lyrical segments—Naming the Flowers(报花名) and Praising Yue’e(夸月娥)—from the Pingju opera A Flower as Matchmaker 《花为媒》as its corpus, and compares the English subtitles generated by two mainstream LLMs. The analysis focuses on three dimensions: linguistic style, cultural imagery, and communicative adaptability. By integrating Newmark’s theories of semantic and communicative translation and Nida’s theory of functional equivalence, the study evaluates the strategic tendencies and functional performance of AI models in generating Pingju subtitles. The results show that LLM-1 emphasizes semantic completeness and cultural fidelity, making it suitable for formal performance settings, while LLM-2 features concise language and a brisk rhythm, better aligning with mass communication contexts. This paper aims to provide strategic insights for AI-assisted Pingju translation practice and promote the effective dissemination of regional Chinese opera in international contexts.
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How to cite this paper
A Comparative Study of AI-Assisted English Translation Strategies for Pingju Opera
How to cite this paper: Yanli Zhang, Cuiyun Tang, Ying Guo. (2025). A Comparative Study of AI-Assisted English Translation Strategies for Pingju Opera. Translation and Foreign Language Learning, 1(1), 77-82.
DOI: http://dx.doi.org/10.26855/tfll.2025.08.013