Lexicalizing Affective Meaning in Published and LLM-Generated Literary Translation: A Corpus-Based Study of Chinese Translations of Margaret Mitchell’s Gone with the Wind
DOI:
https://doi.org/10.61360/BoniGHSS262020520408Keywords:
literary translation, affective meaning, LLM translation, lexical explicitness, promptingAbstract
This study examines how affective and evaluative meaning is lexicalized in published Chinese translations and large language model (LLM) outputs of literary prose. The corpus comprises 98 purposively selected English sentences from Margaret Mitchell’s Gone with the Wind, three published Chinese translations, and twelve LLM outputs generated by six systems under a general translation prompt and an emotion-and-style-oriented prompt. Affective explicitness is operationalized through the density and type range of contextually validated entries in the DUTIR Chinese Emotion Ontology; lexical convergence through word- and category-level Jaccard overlap among versions aligned to the same source sentence. The comparisons use 36 complete-case sentence groups in which all 15 versions contain at least one retained DUTIR item. In this sensitivity sample, LLM outputs show higher dictionary-recognized affective-word density and broader type ranges than the published translations, alongside greater within-condition lexical convergence. The second prompt leaves aggregate density largely unchanged, increases distinct-type counts, and reduces cross-model word overlap. The findings treat lexical visibility and convergence as properties of output form rather than proxies for affective intensity, literary quality, or translation superiority.
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