Interpreting quality and effort in expert and novice interpreters
Bibliographic record
Abstract
The question of what expertise in interpreting is has sparked numerous studies. These studies do not only suggest that expert interpreters perform generally better than novices (Dillinger, 1990), but also that they are more successful in dealing with “problem triggers”, such as complex sentence structures (Liu, Schallert, & Carroll, 2004), fast speech (Rosendo & Galván, 2019) or high information density (Hild, 2015). Gile’s Effort models suggest that the superiority of experienced interpreters does not result from lower cognitive effort, but rather from a better coordination of cognitive resources (Gile, 2009; Liu, 2009). The question whether expert interpreters indeed find interpreting less effortful than novice interpreters, however, received less attention. Ongoing data collection in the SNSF-funded CLINT project (Cognitive load in interpreting and translation) allows us to address this question. At the YMLP, I will present a first set of data of 7 professional and 7 student interpreters for the investigation of the effect of expertise on interpreting. The participants, all German native speakers, interpreted a speech from English to German. The source speech is an authentic speech that was delivered at a conference on energy-related matters. It was recorded, transcribed and re-spoken by a Canadian native speaker in order to obtain clear sound without ambient noise. After interpretation, participants assessed the cognitive demands or the effort they perceived to be involved in the task by means of the NASA-TXL (Hart & Staveland, 1988). Based on previous studies, we expect to find higher interpreting quality in expert than in novice interpreters but no difference between experts’ and novices’ effort ratings as predicted by Gile’s Effort models. In order to triangulate interpreting quality ratings and participants’ effort ratings, we developed a new method for the quantitative assessment of sense consistency and completeness of a target speech over time. Sense consistency and completeness, although reflecting the multidimensional concept of interpreting quality only partially, are regarded by interpreters (Tiselius, 2010; Zwischenberger, 2010) and users (Pradas Macías, 2006) as two major aspects of interpreting quality. The newly designed method has been used on a first set of data and will be presented alongside participants’ effort ratings.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".