Generative Adversarial learning with Negative Data Augmentation for Semi-supervised Text Classification
Bibliographic record
Abstract
In recent years, semi-supervised generative adversarial networks (SS-GANs) models such as GAN-BERT have achieved promising results on the text classification task. One of the techniques used in these models to mitigate the generator from mode collapse is feature matching (FM). Although FM addresses some of the critical issues of SS-GANs, these models still suffer from mode collapse with missing coverage outside the data manifold. Moreover, FM loosely tries to match the distribution between the real data and the fake generated samples. By doing this, the generator can generate fake samples inside high-density regions in the data manifold, where the discriminator learns to misclassify them as out-of-data-manifold regions. In this work, we employ the negative data augmentation (NDA) technique, for the first time in text classification, to alleviate the mentioned problems. NDA is a unique way of producing out-of-distribution fake examples by applying mixup transformation on the fake samples and augmented real data. In our new model (NDA-GAN), we produce NDA samples by combining the generator's output with the contextual representation of the real data. As a result of the mixing, NDA samples are less likely to place in the high-density regions, and due to blending with real data representations, these samples reasonably preserve a close distance to the data manifold. Consequently, the NDA samples increase the discriminator's power to find the optimal decision boundary. Our experimental results demonstrate that the negative augmented samples improve the overall accuracy of our proposed model and make it more confident when detecting out-of-distribution samples.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".