A Simple Closed-Class/Open-Class Factorization for Improved Language Modeling.
Bibliographic record
Abstract
We describe a simple improvement to n-gram language models where we estimate the distribution over closed-class (function) words separately from the conditional distribution of open-class words given function words. In English, function words account for about 30% of written language, and also form a natural skeleton for most sentences. By factoring a language model into a function word model and a conditional model over open-class words given function words, we largely avoid the problem of sparse training data in the first phase, and localize the need for sophisticated smoothing techniques primarily to the second conditional model. We test our factored approach on the Brown and Wall Street Journal corpora and observe a 3.5% to 25.2% improvement in perplexity over standard methods, depending on the particular smoothing method and test set used. Compared to other proposals for improving n-gram language models, our factorization has the advantage of inherent simplicity and efficiency, and improves generalization between data sets.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".