Can ChatGPT Break Through the ‘Bastion’ of Literary Translation? A Stylometric Study of Human vs. Machine Translation Variants
Date:
Research Design:
- Comparative analysis of 7 human translations (HT) vs. 14 machine translations (MT)
- Target texts: Two canonical literary essays
- Methodology: Text similarity algorithms for stylometric measurement
Key Discoveries:
- LLMs surpass NMT in stylistic adaptability
- Human translators maintain 37% greater stylistic range than best-performing LLM
- “Translation universals” manifest differently across HT/LLM/NMT
Implications:
- Challenges in automating literary translation
- Quantitative framework for assessing “translation style”
- Benchmark for future LLM development in creative domains