ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection

Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, Erik Cambria, Roger Zimmermann

Abstract
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.
Anthology ID:
D18-1280
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2594–2604
Language:
URL:
https://aclanthology.org/D18-1280
DOI:
10.18653/v1/D18-1280
Bibkey:
Cite (ACL):
Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, Erik Cambria, and Roger Zimmermann. 2018. ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2594–2604, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection (Hazarika et al., EMNLP 2018)
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PDF:
https://preview.aclanthology.org/ingest-bitext-workshop/D18-1280.pdf
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