Multi-level fusion with fine-grained alignment for multimodal sentiment analysis
Bibliographic record
Abstract
Abstract Multimodal sentiment analysis (MSA) integrates and processes data from multiple sources, like audio and text, to better understand human emotions through cross-modal interactions. The effective acquisition and integration of meaningful features for constructing richer sentiment representations remains a key challenge in MSA. Most existing methods directly obtain global representations and integrate at the utterance level from different modalities, but this ignores fine-grained representations and makes it difficult to capture intricate relationships within and between modalities. Therefore, we propose a novel method, Fine-grained Multimodal Fusion Network (MMTA). Firstly, a Fine-grained Alignment (FGA) module is introduced to align and extract word-level features to bridge heterogeneous modal gaps. FGA enables word-level alignment between audio, text, and their corresponding contextual information using the Montreal Forced Aligner (MFA). Secondly, a Multi-level Fusion module (MLF) is designed, which captures more cross-modal interaction through three stages: Local-Local Interaction, Local-Global Interaction, and Similarity-weighted Representation Adjustment. Finally, an Attention Fusion Network(AFN) module is developed to capture both inter- and intra-modal correlations, enabling the generation of consistent multimodal representations. Extensive evaluations on widely used MSA datasets, CMU-MOSI and CMU-MOSEI, indicate that our method outperforms prior baselines and validates the effectiveness of the fine-grained alignment and the multi-level fusion for improving multimodal sentiment analysis performance.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".