The Chinese-English Conference Interpreting Corpus: Uses and Limitations
Bibliographic record
Abstract
This paper presents an overview of the compilation of the Chinese-English Conference Interpreting Corpus followed by an outline of research findings based on data obtained from the corpus. It is argued that interpreting corpora, including the Chinese-English Conference Interpreting Corpus, are called to play an increasingly important role in the study of linguistic features of interpreted texts, interpreting norms and the cognitive process of interpreting. Research based on the Chinese-English Conference Interpreting Corpus suggests that the use of English passive construction, optional connective ‘that’ and the infinitive particle ‘to’ in interpreted texts is demonstrably more frequent than in the translated English texts of the Chinese government’s work reports and the non-translated English texts of press conferences. In a broader sense, interpreted texts exhibit greater tendency towards normalization and explicitation than written translated texts. This paper also touches on the limitations that have been observed while working with interpreting corpora. These limitations are in a large measure related to the difficulty in transcribing nonverbal aspects of the interpreting activity, including the speaker’s tone and facial expressions, as well as the audience’s facial expressions. These aspects have a clear effect on interpreter’s choice/use of interpreting strategies and methods, so they merit careful consideration in interpreting studies.
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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 | 0.039 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".