ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
By Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, Erik Cambria, Roger Zimmermann. Conference on Empirical Methods in Natural Language Processing, 2018
416 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/93876ad69006c09bb35077340003233cc3cf3709
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Soujanya Poria
- Erik Cambria
- Rada Mihalcea
- Navonil Majumder
- Devamanyu Hazarika
- Louis-philippe Morency
- Bo Xu
- Percy Liang
In Experimental and Cognitive Psychology
- Soujanya Poria
- Navonil Majumder
- Devamanyu Hazarika
- Rada Mihalcea
- Erik Cambria
- Bo Xu
- Shuang Xu
- Xiuyi Chen
Citations in the corpus
- MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations (2018, methodology): “Recently, Hazarika et al. (2018) proposed a multi-modal memory network that can recognize emotion in dyadic dialogues.”
- Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances (2019, background): “[23], strive to address several key research challenges that make the task of ERC difficult to solve:”
- Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (2020, background): “Later, Hazarika et al. (2018a) improves upon this approach with interactive conversational memory network (ICON), which utilizes the interactive memory unit to hierarchically model the self-and inter-speaker emotional influences for emotion recognition in conversational videos.”
- Recent Advances and Trends in Multimodal Deep Learning: A Review (2021, background): “D. Hazarika et al. [52] proposed an emotion recognition framework from video conversations.”