Integrating CAI Tools & ASR in the Interpreting Classroom: A Proposal for Lesson Plans Using InterpretBank
Bibliographic record
Abstract
Technology advances have brought some important changes to interpreting practice, such as making distance interpreting possible and creating useful computer-assisted interpreting (CAI) tools. Tools such as InterpretBank that include features like terminology building and automatic speech recognition (ASR) are advantageous to interpreters in various settings. This study aims to present an overview of the CAI tools and their development, analyze the current market demand for technology-related skills, and integrate CAI and ASR tools with interpreter education. To analyze technology requirements in the job market of interpreting, we collected twenty-four job advertisements and examined employers’ demand for the aspects of distance interpreting and other technology-related requirements. This study then proposes a lesson plan that integrates a CAI tool (InterpretBank) into an interpreting course for MA-level students. The lesson plan contains three modules: the first one introduces the basic concepts of CAI tools and interpreting remotely; the second module focuses on the terminology-building feature of CAI tools, especially in creating multilingual glossaries and extracting terminology in the preparation stage of interpreting; the third module highlights ASR feature of InterpretBank. Keywords: CAI tools, interpreting, InterpretBank, terminology, speech recognition.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".