Lexical Cohesion in English-Chinese Business Translation: Human Translators Versus ChatGPT
Bibliographic record
Abstract
Lexical cohesion involves the continuity of text on the level of lexis achieved through word choices; it embodies repetition, synonymy, and collocation (Halliday, 1985). This study attempts to elucidate the lexical cohesion and examine translation shifts in the English-Chinese translation of business texts. The English business texts are compared with two Chinese translations: one by human translators and the other by ChatGPT. The research is based on Halliday’s (1985) cohesion and Toury’s (2012) descriptive translation studies. The data of lexical cohesion are identified and collected manually in a parallel corpus. The analysis deals with the description of lexical cohesion and translation shifts. The research reveals that semantic meanings of the items of lexical cohesion are largely maintained in the English-Chinese business translation. Additionally, despite using translation methods like literal translation, addition, omission, and conversion in translating lexical cohesion, human translators make more translation shifts of lexical cohesion than ChatGPT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".