Using Roark-Hollingshead Distance to Probe BERT’s Syntactic Competence
Bibliographic record
Abstract
Probing BERT's general ability to reason about syntax is no simple endeavour, primarily because of the uncertainty surrounding how large language models represent syntactic structure.Many prior accounts of BERT's agility as a syntactic tool (Clark et al., 2013;Lau et al., 2014;Marvin and Linzen, 2018;Chowdhury and Zamparelli, 2018;Warstadt et al., 2019Warstadt et al., , 2020;;Hu et al., 2020) have therefore confined themselves to studying very specific linguistic phenomena, and there has still been no definitive answer as to whether BERT "knows" syntax.The advent of perturbed masking (Wu et al., 2020) would then seem to be significant, because this is a parameter-free probing method that directly samples syntactic trees from BERT's embeddings.These sampled trees outperform a right-branching baseline, thus providing preliminary evidence that BERT's syntactic competence bests a simple baseline.This baseline is underwhelming, however, and our reappraisal below suggests that this result, too, is inconclusive.We propose RH Probe, an encoder-decoder probing architecture that operates on two probing tasks.We find strong empirical evidence confirming the existence of important syntactic information in BERT, but this information alone appears not to be enough to reproduce syntax in its entirety.Our probe makes crucial use of a conjecture made by Roark and Hollingshead (2008) that a particular lexical annotation that we shall call RH distance is a sufficient encoding of unlabelled binary syntactic trees, and we prove this conjecture.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".