DFMU: Distribution-based Framework for Modeling Aleatoric Uncertainty in Multimodal Sentiment Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Multimodal Sentiment Analysis (MSA), data noise arising from various sources can lead to uncertainty in Aleatoric Uncertainty (AU), significantly impacting model performance. Current efforts to address AU have insufficiently explored its sources. They primarily focus on modeling noise rather than implementing targeted modeling based on its origin. Consequently, these approaches struggle to effectively mitigate the influence of AU, resulting in sustained limitations in model performance. Our research identifies that the AU primarily stems from two problems: subjective bias in the annotation process and the complex set relationships of sentiment features. To specifically address them, we propose DFMU, a Distribution-based Framework for Modeling Aleatoric Uncertainty, which incorporates an uncertainty modeling block capable of encoding uncertainty distributions and adaptively adjusting optimization objectives. Furthermore, we introduce distribution-based contrastive learning with sentiment words replacement to better capture the complex relationships among features. Extensive experiments on three public MSA datasets, i.e., MOSI, MOSEI, and SIMS, demonstrate that the proposed model maintains robust performance even under high noise conditions and achieves state-of-the-art results on these popular datasets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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