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        <title>ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection</title>
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        <namePart type="given">Devamanyu</namePart>
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        <namePart type="given">Soujanya</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
%A Hazarika, Devamanyu
%A Poria, Soujanya
%A Mihalcea, Rada
%A Cambria, Erik
%A Zimmermann, Roger
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F hazarika-etal-2018-icon
%X 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.
%R 10.18653/v1/D18-1280
%U https://aclanthology.org/D18-1280
%U https://doi.org/10.18653/v1/D18-1280
%P 2594-2604

Markdown (Informal)

[ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection](https://aclanthology.org/D18-1280) (Hazarika et al., EMNLP 2018)

• ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection (Hazarika et al., EMNLP 2018)

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.