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arXiv · 1907.04197

Attending to Emotional Narratives

Zhengxuan Wu·Xiyu Zhang·Tan Zhi-Xuan·Jamil Zaki·Desmond C. Ong
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Abstract

Attention mechanisms in deep neural networks have achieved excellent performance on sequence-prediction tasks. Here, we show that these recently-proposed attention-based mechanisms---in particular, the Transformer with its parallelizable self-attention layers, and the Memory Fusion Network with attention across modalities and time---also generalize well to multimodal time-series emotion recognition. Using a recently-introduced dataset of emotional autobiographical narratives, we adapt and apply these two attention mechanisms to predict emotional valence over time. Our models perform extremely well, in some cases reaching a performance comparable with human raters. We end with a discussion of the implications of attention mechanisms to affective computing.

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Zhengxuan Wu, Xiyu Zhang, Tan Zhi-Xuan, Jamil Zaki, Desmond C. Ong. 2019-07-08. Attending to Emotional Narratives. https://arxiv.org/abs/1907.04197

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