Contrasting Bilingual and Monolingual Idiom Processing
Bibliographic record
Abstract
In this chapter, we survey what is currently known about bilingual idiom processing and present data from a study that investigates three questions about the comprehension of idioms in English-French bilinguals. First, do the linguistic factors that control monolingual idiom comprehension (e.g., familiarity, literal plausibility, semantic decomposability; Libben & Titone, 2008) similarly control bilingual comprehension? Second, does an idiom’s cross-language similarity affect comprehension? Third, does native language status interact with idiom processing in these respects? To address these questions, we conducted a comprehension study where English-French bilinguals read English sentences that included idioms from a prior normative first-language study that were further coded for their similarity to idioms in French. We also manipulated whether the idiom-final word was presented in English (intact condition) or French (code-switched condition). The results suggest that bilinguals are sensitive to the same linguistic factors that control idiom processing for monolinguals (i.e., familiarity) and that previous work suggesting an increased role for semantic decomposability (Abel, 2003) may actually be due to cross-language overlap. The implications for bilingual lexical representation and processing are discussed. Keywords : bilingualism, idiom processing, code-switching, figurative language processing, idiomatic expressions When Joan Foster visited her Polish lover, Paul, she stumbled upon several English novels penned by an improbably named Mavis Quilp.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".