The meaning of intonation in yes-no questions in American English: A corpus study
Bibliographic record
Abstract
Abstract In order to investigate the distinct nuances of meaning conveyed by the different intonational contours encountered in yes-no questions in English, we conducted a corpus study of the intonation of 410 naturally occurring spoken interrogative-form yes-no questions in American English. First we annotated the intonation of each question using ToBI and then examined the meaning of each utterance in the context. We found that the low-rise nuclear contour (e.g., L*H-H%) is the unmarked question contour and is by far the most frequently occurring. Yes-no questions with falling intonation (e.g. H*L-L%) do not occur frequently, but when they do, they can be classified in speech act terms as “non-genuine” questions, where one or more felicity conditions on genuine questions are not met. Level questions (e.g., L*H-L%) tend to be “stylized” in meaning and pattern with falling questions in being non-genuine. We also found that the pitch accent on high-rise questions (e.g., H*H-H%), where the final pitch contour starts high and ends higher, tends to mark information that is given in the discourse or a function word. These are syllables that would normally remain unaccented parts of the post-nuclear “tail” of the intonation phrase. This leads us to propose that many such accents are “post-nuclear accents” in the sense of Ladd 2008.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".