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

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


Abstract
Multimodal affective computing, learning to recognize and interpret human affect and subjective information from multiple data sources, is still a challenge 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 levels, ignoring time-dependent interactions between modalities. Addressing such issues, we introduce a hierarchical multimodal architecture with attention and word-level fusion to classify utterance-level sentiment and emotion from text and audio data. Our introduced model outperforms state-of-the-art approaches on published datasets, and we demonstrate that our model is able to visualize and interpret synchronized attention over modalities.
Anthology ID:
P18-1207
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2225–2235
Language:
URL:
https://aclanthology.org/P18-1207
DOI:
10.18653/v1/P18-1207
Bibkey:
Cite (ACL):
Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, and Ivan Marsic. 2018. Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2225–2235, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment (Gu et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1207.pdf
Presentation:
 P18-1207.Presentation.pdf
Video:
 https://aclanthology.org/P18-1207.mp4
Data
IEMOCAPMultimodal Opinionlevel Sentiment Intensity