DialogueGCN: A graph convolutional neural network for emotion recognition in conversation

Deepanway Ghosal; Navonil Majumder; Soujanya Poria; Niyati Chhaya; Alexander Gelbukh

DialogueGCN: A graph convolutional neural network for emotion recognition in conversation

Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, Alexander Gelbukh

Centro de Investigación en Computación (CIC)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

286 Citas (Scopus)

Resumen

Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based approach to ERC. We leverage self and inter-speaker dependency of the interlocutors to model conversational context for emotion recognition. Through the graph network, DialogueGCN addresses context propagation issues present in the current RNN-based methods. We empirically show that this method alleviates such issues, while outperforming the current state of the art on a number of benchmark emotion classification datasets.

Idioma original	Inglés
Título de la publicación alojada	EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference
Editorial	Association for Computational Linguistics
Páginas	154-164
Número de páginas	11
ISBN (versión digital)	9781950737901
Estado	Publicada - 2019
Evento	2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019 - Hong Kong, China Duración: 3 nov. 2019 → 7 nov. 2019

Serie de la publicación

Nombre	EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference

Conferencia

Conferencia	2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019
País/Territorio	China
Ciudad	Hong Kong
Período	3/11/19 → 7/11/19

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Ghosal, D., Majumder, N., Poria, S., Chhaya, N., & Gelbukh, A. (2019). DialogueGCN: A graph convolutional neural network for emotion recognition in conversation. En EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference (pp. 154-164). (EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference). Association for Computational Linguistics.

Ghosal, Deepanway ; Majumder, Navonil ; Poria, Soujanya et al. / DialogueGCN : A graph convolutional neural network for emotion recognition in conversation. EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference. Association for Computational Linguistics, 2019. pp. 154-164 (EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference).

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title = "DialogueGCN: A graph convolutional neural network for emotion recognition in conversation",

abstract = "Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based approach to ERC. We leverage self and inter-speaker dependency of the interlocutors to model conversational context for emotion recognition. Through the graph network, DialogueGCN addresses context propagation issues present in the current RNN-based methods. We empirically show that this method alleviates such issues, while outperforming the current state of the art on a number of benchmark emotion classification datasets.",

author = "Deepanway Ghosal and Navonil Majumder and Soujanya Poria and Niyati Chhaya and Alexander Gelbukh",

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Ghosal, D, Majumder, N, Poria, S, Chhaya, N & Gelbukh, A 2019, DialogueGCN: A graph convolutional neural network for emotion recognition in conversation. En EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference. EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, Association for Computational Linguistics, pp. 154-164, 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, 3/11/19.

DialogueGCN: A graph convolutional neural network for emotion recognition in conversation. / Ghosal, Deepanway; Majumder, Navonil; Poria, Soujanya et al.
EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference. Association for Computational Linguistics, 2019. p. 154-164 (EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - DialogueGCN

T2 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019

AU - Ghosal, Deepanway

AU - Majumder, Navonil

AU - Poria, Soujanya

AU - Chhaya, Niyati

AU - Gelbukh, Alexander

PY - 2019

Y1 - 2019

N2 - Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based approach to ERC. We leverage self and inter-speaker dependency of the interlocutors to model conversational context for emotion recognition. Through the graph network, DialogueGCN addresses context propagation issues present in the current RNN-based methods. We empirically show that this method alleviates such issues, while outperforming the current state of the art on a number of benchmark emotion classification datasets.

AB - Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based approach to ERC. We leverage self and inter-speaker dependency of the interlocutors to model conversational context for emotion recognition. Through the graph network, DialogueGCN addresses context propagation issues present in the current RNN-based methods. We empirically show that this method alleviates such issues, while outperforming the current state of the art on a number of benchmark emotion classification datasets.

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M3 - Contribución a la conferencia

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EP - 164

BT - EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference

PB - Association for Computational Linguistics

Y2 - 3 November 2019 through 7 November 2019

ER -

Ghosal D, Majumder N, Poria S, Chhaya N, Gelbukh A. DialogueGCN: A graph convolutional neural network for emotion recognition in conversation. En EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference. Association for Computational Linguistics. 2019. p. 154-164. (EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference).

DialogueGCN: A graph convolutional neural network for emotion recognition in conversation

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