There might be more to syntactic bootstrapping than being pragmatic: A look at grammatical person and prosody in naturalistic child-directed speech
Bibliographic record
Abstract
In 'Being pragmatic about syntactic bootstrapping', Hacquard (2022) argues that abstract syntax is useful for word learning, but that an additional cue, pragmatics, is both necessary and available for young children during the first steps of language acquisition. She focuses on modals and attitude verbs, where the physical context seems particularly impoverished as the sole basis for deriving meanings, and thus where linguistic cues may be particularly helpful. She convincingly shows how pragmatic and syntactic cues could be combined to help young language learners learn and infer the possible meanings of attitude verbs such as "think", "know" or "want". She also argues that, in some circumstances, syntax and pragmatics would need to be supplemented by semantic information from context - for instance, in the case of modals such as "might", "can", or "must". We agree with Hacquard on the importance of the synergies between these different cues to meaning, and wish to add two other aspects of the input that might also be used by young children in these contexts. The aspects we describe can only be noticed when one analyzes concrete examples of what children hear in their everyday lives, something which Hacquard does very often in her work (e.g., Dieuleveut, van Dooren, Cournane & Hacquard, 2022; Huang, White, Liao, Hacquard & Lidz, 2022; Yang, 2022). Taking into account different cues for meaning would help the field go beyond current models of syntactic bootstrapping, and create an integrated picture of the synergies between different levels of linguistic information.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 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".