Acquiring constraints on filler-gap dependencies from structural collocations: Assessing a computational learning model of island-insensitivity in Norwegian
Bibliographic record
Abstract
Children induce complex syntactic knowledge from their native language input. A long-standing discussion focuses on types of learning biases that help them arrive at correct generalization and solve induction problems posed by impoverished input. Studies employing computational models for learning specific language phenomena serve as testing grounds for evaluating types of biases required for successful acquisition. Recent work by Pearl & Sprouse (2013b) demonstrates that a distributional learner that tracks trigrams over structurally annotated input can acquire wh-filler-gap dependencies and island constraints on them in English. Though intriguing, it is unclear yet whether a similar distributional learning model is a viable mechanism for learning island facts in other languages given the possibility of cross-linguistic variation. In this study, we explore whether a distributional learner can acquire wh- and relative clause filler-gap dependencies and island constraints in Norwegian from child-directed annotated text. We find that the proposed learning strategy can capture some patterns of island-insensitivity in Norwegian while failing to learn others due to a lack of relevant data in the input. Our findings suggest that given limited input data, a simple n-gram-based distributional learning over structured representations may not be sufficient to fully recover human-like knowledge of filler-gap dependency relations and island constraints cross-linguistically.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".