Interpretation of Discourse Connectives Is Probabilistic: Evidence From the Study of <i>But</i> and <i>Although</i>
Bibliographic record
Abstract
Connectives can facilitate the processing of discourse relations by helping comprehenders to infer the intended coherence relation holding between two text spans. Previous experimental studies have focused on pairs of connectives that are very different from one another to be able to compare and formalize the distinguishing effects of these particles in discourse comprehension. In this article, we compare two connectives, but and although, which overlap in terms of the relations they can signal. We demonstrate in a set of carefully controlled studies that while a connective can be a marker of several discourse relations, it can have a specific fine-grained biasing effect on linguistic inferences and that this bias can be derived (or predicted) from the connectives’ distribution of relations found in production data. The effects that we find speak to the ambiguity of discourse connectives, in general, and the different functions of but and although, in particular. These effects cannot be explained within the earlier accounts of discourse connectives, which propose that each connective has a core meaning or processing instruction. Instead, we here lay out a probabilistic account of connective meaning and interpretation, which is based on the distribution of connectives in production and is supported by our experimental findings.
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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 | 0.021 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".