Interpreting into an SOV Language: Memory and the Position of the Verb. A Corpus-Based Comparative Study of Interpreted and Non-mediated Speech
Bibliographic record
Abstract
In Dutch and German subordinate clauses, the verb is generally placed after the clausal constituents (Subject-Object-Verb structure) thereby creating a middle field (or verbal brace). This makes interpreting from SOV into SVO languages particularly challenging as it requires further processing and feats of memory. It often requires interpreters to use specific strategies (for example, anticipation) (Lederer 1981; Liontou 2011). However, few studies have tackled this issue from the point of view of interpreting into SOV languages. Producing SOV structures requires some specific cognitive effort as, for instance, subject properties need to be kept in mind in order to ensure the correct subject-verb agreement across a span of 10 or 20 words. Speakers therefore often opt for a strategy called extraposition, placing specific elements after the verb in order to shorten the brace (Hawkins 1994; Bevilacqua 2009). Dutch speakers use this strategy more often than German speakers (Haeseryn 1990). Given the additional cognitive load generated by the interpreting process (Gile 1999), it may be assumed that interpreters will shorten the verbal brace to a larger extent than native speakers. The present study is based on a corpus of interpreted and non-mediated speeches at the European Parliament and compares middle field lengths as well as extraposition in Dutch and German subordinate clauses. Results from 3460 subordinate clauses confirm that interpreters of both languages shorten the middle field more than native speakers. The study also shows that German interpreters use extraposition more often than native speakers, but this is not the case for Dutch interpreters. Dutch and German interpreters appear to use extraposition partly because they imitate the clause word order of the source speech, showing that, in this case, extraposition can be considered an effort-saving tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".