On Hierarchical Re-ordering and Permutation Parsing for Phrase-based Decoding
Bibliographic record
Abstract
The addition of a deterministic permutation parser can provide valuable hierarchical information to a phrase-based statistical machine translation (PBSMT) system. Permutation parsers have been used to implement hierarchical re-ordering models (Galley and Manning, 2008) and to enforce inversion transduction grammar (ITG) constraints (Feng et al., 2010). We present a number of theoretical results regarding the use of permutation parsers in PBSMT. In particular, we show that an existing ITG constraint (Zens et al., 2004) does not prevent all non-ITG permutations, and we demonstrate that the hierarchical reordering model can produce analyses during decoding that are inconsistent with analyses made during training. Experimentally, we verify the utility of hierarchical re-ordering, and compare several theoretically-motivated variants in terms of both translation quality and the syntactic complexity of their output. 1
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".