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ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection - 2018

Research Area:  Machine Learning


Emotion recognition in conversations is crucial for building empathetic machines. Present works in this domain do not explicitly consider the inter-personal influences that thrive in the emotional dynamics of dialogues. To this end, we propose Interactive COnversational memory Network (ICON), a multimodal emotion detection framework that extracts multimodal features from conversational videos and hierarchically models the self- and inter-speaker emotional influences into global memories. Such memories generate contextual summaries which aid in predicting the emotional orientation of utterance-videos. Our model outperforms state-of-the-art networks on multiple classification and regression tasks in two benchmark datasets.

Author(s) Name:  Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, Erik Cambria, Roger Zimmermann

Journal name:  Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Conferrence name:  

Publisher name:  ACL

DOI:  10.18653/v1/D18-1280

Volume Information: