Large-Scale Learning of Embeddings with Reconstruction Sampling
Bibliographic record
Abstract
In this paper, we present a novel method to speed up the learning of embeddings for large-scale learning tasks involving very sparse data, as is typically the case for Natural Language Processing tasks. Our speed-up method has been developed in the context of Denoising Auto-encoders, which are trained in a purely unsupervised way to capture the input distribution, and learn embeddings for words and text similar to earlier neural language models. The main contribution is a new method to approximate reconstruction error by a sampling procedure. We show how this approximation can be made to obtain an unbiased estimator of the training criterion, and we show how it can be leveraged to make learning much more computationally efficient. We demonstrate the effectiveness of this method on the Amazon and RCV1 NLP datasets. Instead of reducing vocabulary size to make learning practical, our method allows us to train using very large vocabularies. In particular, reconstruction sampling requires 22x less training time on the full Amazon dataset.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".