Cross-lingual Transfer for Unsupervised Dependency Parsing Without Parallel Data
Why this work is in the frame
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Bibliographic record
Abstract
Cross-lingual transfer has been shown to produce good results for dependency parsing of resource-poor languages. Although this avoids the need for a target language treebank, most approaches have still used large parallel corpora. However, parallel data is scarce for low-resource languages, and we report a new method that does not need parallel data. Our method learns syntactic word embeddings that generalise over the syntactic contexts of a bilingual vocabulary, and incorporates these into a neural network parser. We show empirical improvements over a baseline delexicalised parser on both the CoNLL and Universal Dependency Treebank datasets. We analyse the importance of the source languages, and show that combining multiple source-languages leads to a substantial improvement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it