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arXiv · 1805.08660

Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment

Yue Gu·Kangning Yang·Shiyu Fu·Shuhong Chen·Xinyu Li·Ivan Marsic
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Abstract

Multimodal affective computing, learning to recognize and interpret human affects and subjective information from multiple data sources, is still challenging because: (i) it is hard to extract informative features to represent human affects from heterogeneous inputs; (ii) current fusion strategies only fuse different modalities at abstract level, ignoring time-dependent interactions between modalities. Addressing such issues, we introduce a hierarchical multimodal architecture with attention and word-level fusion to classify utter-ance-level sentiment and emotion from text and audio data. Our introduced model outperforms the state-of-the-art approaches on published datasets and we demonstrated that our model is able to visualize and interpret the synchronized attention over modalities.

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Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, Ivan Marsic. 2018-05-22. Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment. https://arxiv.org/abs/1805.08660

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