Noisy Multi-Label Aggregation With Self-Supervised Graph Transformer in Mobile Crowdsourcing
Bibliographic record
Abstract
Aggregating noisy labels from mobile crowdsourcing (MCS) to recover true labels is a fundamental yet challenging problem, especially due to the sparsity and unreliability of crowd-contributed data. While most prior work addresses only single-label scenarios, real-world MCS applications often require robust solutions for both single-label and multi-label tasks, where each instance may be associated with multiple categories. In this paper, we propose ATHENA, a novel approach that leverages self-supervision signals inherent in MCS data for effective label aggregation. Firstly, we propose a graph transformer model that can learn from the MCS topology and features. Then, we propose self-supervision signals inherently included in the dataset to help aggregate the labels. To address the unique challenges of multi-label aggregation, we further extend our approach toATHENA+, introducing a label message passing (LMP) module that explicitly models correlations and dependencies among labels. We conducted extensive experiments on multiple single-label and multi-label classification datasets, comparing the proposed models with state-of-the-art methods. Our results demonstrate that ATHENA and ATHENA+ are highly effective in aggregating labels and obtain much better performance than existing methods.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".