Learning Representations through Contrastive Strategies for a more Robust Stance Detection
Bibliographic record
Abstract
Stance Detection refers to the process of determining an author’s position towards a particular issue or target in a text. Previous research suggests that existing systems for Stance Detection are not resilient enough to handle variations and errors in input sentences. In our proposed methodology, we utilize Contrastive Learning to learn sentence representations. We achieve this by bringing semantically similar sentences and those implying the same stance closer to each other in the embedding space. To compare our approach, we use a pretrained transformer model that is directly finetuned with the stance datasets. We evaluate the resilience of the models using char-level and word-level adversarial perturbation attacks and show that our approach performs better and is more robust to the different adversarial perturbations introduced to the test data. Our approach is also shown to perform better on small-sized and class-imbalanced stance datasets. We further experiment with unlabeled stance datasets to make the representation learning independent of domain-specific labels, and the models trained with our approach on unlabeled datasets are still robust and perform comparably to those trained with labeled data.
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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 | 0.002 | 0.006 |
| 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 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".