Condition-Transforming Variational Autoencoder for Generating Diverse Short Text Conversations
Bibliographic record
Abstract
In this article, conditional-transforming variational autoencoders (CTVAEs) are proposed for generating diverse short text conversations. In conditional variational autoencoders (CVAEs), the prior distribution of latent variable z follows a multivariate Gaussian distribution with mean and variance modulated by the input conditions. Previous work found that this distribution tended to become condition-independent in practical applications. Thus, this article designs CTVAEs to enhance the influence of conditions in CVAEs. In a CTVAE model, the latent variable z is sampled by performing a non-linear transformation on the combination of the input conditions and the samples from a condition-independent prior distribution N (0, I). In our experiments using a Chinese Sina Weibo dataset, the CTVAE model derives z samples for decoding with better condition-dependency than that of the CVAE model. The earth mover’s distance (EMD) between the distributions of the latent variable z at the training stage, and the testing stage is also reduced by using the CTVAE model. In subjective preference tests, our proposed CTVAE model performs significantly better than CVAE and sequence-to-sequence (Seq2Seq) models on generating diverse, informative, and topic-relevant responses.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".