Discourse coherence and the interpretation of accented pronouns
Bibliographic record
Abstract
It has long been argued that accenting or stressing a pronoun (i.e., making it prosodically prominent) changes its interpretation as compared to its unaccented counterpart. However, recent experimental work demonstrated that this generalization does not apply when the alternative interpretation of the pronoun is not plausible (Taylor et al., 2013). In a series of three experiments that use an offline comprehension task, we show, first, that the lack of reversal is observed when plausibility is controlled for. We furthermore show that a new generalization cannot be formed by excluding cases where the bias towards the unmarked interpretation is strong or cases where the character in the alternative interpretation is low in salience. Instead, we conclude that what constrains the interpretation of accented pronouns is coherence relations, with parallel discourses exhibiting reversal and result discourses not exhibiting reversal. We propose that the difference between coherence relations should be viewed in what would be the minimal change in order to create a ‘surprising’ or expected’ event, which is the characteristic of accenting more generally.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".