Reliability-Aware Multimodal Sentiment Prediction Under Local Modality Missingness
Minghao Kong, Deng Yao, Xi Zhang, Ying Gao, Jiurun Chen, Zirong Liu, Rongjie Wang
Abstract
Multimodal sentiment models are usually evaluated on complete utterances, yet streams often lose a contiguous span of text, audio, or video. We study local missingness on aligned CMU-MOSEI features. Our reliability-aware network (RM-Net) keeps the original temporal positions, represents unobserved positions with a learned token, outputs bounded per-modality intensity contributions gated by observation quality, and is trained with dual-view masking of contiguous segments. We fuse five RM-Net seeds with a LoRA-adapted BERT text expert, using a fusion weight that depends only on the visible fraction of content tokens. Architecture, checkpoint, and fusion choices were made on the validation split. On the held-out split, the fused model reaches a macro-F1 of 0.655 on clean inputs and 0.634 under 40\% mixed missingness; its two-view average exceeds the auxiliary-head baseline by 0.024 (95\% video-cluster bootstrap interval [0.003, 0.045]). Because this split was inspected during development, these numbers are not a blind test. For explanations, exact Shapley attributions over the eight modality subsets and position-level occlusion show that top-ranked positions reduce the predicted-class probability faster than random positions (AUC gap 0.113 on validation). We also describe an auditable word-level alignment pipeline for raw clips; its outputs do not feed the predictive models.