@inproceedings{saxena-etal-2022-static,
title = "Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation",
author = "Saxena, Prakhar and
Huang, Yin Jou and
Kurohashi, Sadao",
editor = "Ippolito, Daphne and
Li, Liunian Harold and
Pacheco, Maria Leonor and
Chen, Danqi and
Xue, Nianwen",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-srw.31/",
doi = "10.18653/v1/2022.naacl-srw.31",
pages = "247--253",
abstract = "Each person has a unique personality which affects how they feel and convey emotions. Hence, speaker modeling is important for the task of emotion recognition in conversation (ERC). In this paper, we propose a novel graph-based ERC model which considers both conversational context and speaker personality. We model the internal state of the speaker (personality) as Static and Dynamic speaker state, where the Dynamic speaker state is modeled with a graph neural network based encoder. Experiments on benchmark dataset shows the effectiveness of our model. Our model outperforms baseline and other graph-based methods. Analysis of results also show the importance of explicit speaker modeling."
}
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<abstract>Each person has a unique personality which affects how they feel and convey emotions. Hence, speaker modeling is important for the task of emotion recognition in conversation (ERC). In this paper, we propose a novel graph-based ERC model which considers both conversational context and speaker personality. We model the internal state of the speaker (personality) as Static and Dynamic speaker state, where the Dynamic speaker state is modeled with a graph neural network based encoder. Experiments on benchmark dataset shows the effectiveness of our model. Our model outperforms baseline and other graph-based methods. Analysis of results also show the importance of explicit speaker modeling.</abstract>
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%0 Conference Proceedings
%T Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation
%A Saxena, Prakhar
%A Huang, Yin Jou
%A Kurohashi, Sadao
%Y Ippolito, Daphne
%Y Li, Liunian Harold
%Y Pacheco, Maria Leonor
%Y Chen, Danqi
%Y Xue, Nianwen
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
%D 2022
%8 July
%I Association for Computational Linguistics
%C Hybrid: Seattle, Washington + Online
%F saxena-etal-2022-static
%X Each person has a unique personality which affects how they feel and convey emotions. Hence, speaker modeling is important for the task of emotion recognition in conversation (ERC). In this paper, we propose a novel graph-based ERC model which considers both conversational context and speaker personality. We model the internal state of the speaker (personality) as Static and Dynamic speaker state, where the Dynamic speaker state is modeled with a graph neural network based encoder. Experiments on benchmark dataset shows the effectiveness of our model. Our model outperforms baseline and other graph-based methods. Analysis of results also show the importance of explicit speaker modeling.
%R 10.18653/v1/2022.naacl-srw.31
%U https://aclanthology.org/2022.naacl-srw.31/
%U https://doi.org/10.18653/v1/2022.naacl-srw.31
%P 247-253
Markdown (Informal)
[Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation](https://aclanthology.org/2022.naacl-srw.31/) (Saxena et al., NAACL 2022)
ACL