Zero-Shot Learning and Clustering for Semantic Utterance Classification
Why this work is in the frame
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Bibliographic record
Abstract
We propose two novel zero-shot learning methods for semantic utterance classification (SUC) using deep learning. Both approaches rely on learning deep semantic embeddings from a large amount of Query Click Log data obtained from a search engine. Traditional semantic utterance classification systems require large amounts of labelled data, whereas our proposed methods make use of the structure of the task to allow classification without labeled data. We also develop a zero-shot semantic clustering algorithm for extracting discriminative features for supervised semantic utterance classification systems. We demonstrate the effectiveness of the zero-shot semantic learning algorithm on the SUC dataset collected by [1]. Furthermore, we show that extracting features using zero-shot semantic clustering for a linear SVM reaches state-of-the-art result on that dataset.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it