Meta Noise Adaption Framework for Multimodal Sentiment Analysis With Feature Noise

Improving the robustness of models against feature noise has emerged as one of the most crucial research topics in the field of multimodal sentiment analysis. Recent studies assume that the training instances are free of noise and develop either translation or reconstruction based method under the guidance of perfect training data for robust testing time performance. However, such an ideal assumption neglects the potential presence of the feature noise in training instances and inevitably results in degradation for the scenario where high-quality training instances are unavailable. In order to achieve robust training with noisy instances, we propose the Meta Noise Adaption (Meta-NA) learning strategy, a meta learning method accumulating the experience of dealing with various types of feature noise. Specifically, we first formulate the tasks distribution where each task is corresponding to one specific pattern of noise, and propose the feature adaption module adding on the unimodal encoder in late fusion based architecture. Through an nested online optimization between the auxiliary feature adaption module and the late fusion backbone modules, the proposed method can leverage shared knowledge across different noisy source tasks and learn how to learn from the noisy instances for robust testing performances. Extensive experiments are conducted on two benchmark multimodal sentiment analysis datasets, namely MOSI and CH-SIMS v2. The results demonstrate that our proposed method can rapidly adapt to various unseen types of feature noise and outperforms all baseline methods, particularly when the training instances are limited.