Neural Networks Can Learn Patterns of Island-insensitivity in Norwegian
Bibliographic record
Abstract
Recent research suggests that Recurrent Neural Networks (RNNs) can capture abstract generalizations about filler-gap dependencies (FGDs) in English and so-called island constraints on their distribution (Wilcox et al., 2018; 2021). These results have been interpreted as evidence that it is possible, in principle, to induce complex syntactic knowledge from the input without domain-specific learning biases. However, the English results alone do not establish that island constraints were induced from distributional properties of the training data instead of simply reflecting architectural limitations independent of the input to the models. We address this concern by investigating whether such models can learn the distribution of acceptable FGDs in Norwegian, a language that is sensitive to fewer islands than English (Christensen, 1982). Results from five experiments show that Long Short-Term Memory (LSTM) RNNs can (i) learn that Norwegian FGD formation is unbounded, (ii) recover the island status of temporal adjunct and subject islands, and (iii) learn that Norwegian, unlike English, permits FGDs into two types of embedded questions. The fact that LSTM RNNs can learn cross-linguistic differences in island facts therefore strengthens the claim that RNN language models can induce the constraints from patterns in the input.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".