@inproceedings{mao-etal-2021-dialoguetrm-exploring,
title = "{D}ialogue{TRM}: Exploring Multi-Modal Emotional Dynamics in a Conversation",
author = "Mao, Yuzhao and
Liu, Guang and
Wang, Xiaojie and
Gao, Weiguo and
Li, Xuan",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.229",
doi = "10.18653/v1/2021.findings-emnlp.229",
pages = "2694--2704",
abstract = "Emotion dynamics formulates principles explaining the emotional fluctuation during conversations. Recent studies explore the emotion dynamics from the self and inter-personal dependencies, however, ignoring the temporal and spatial dependencies in the situation of multi-modal conversations. To address the issue, we extend the concept of emotion dynamics to multi-modal settings and propose a Dialogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics. Specifically, the intra-modal emotion dynamics is to not only capture the temporal dependency but also satisfy the context preference in every single modality. The inter-modal emotional dynamics aims at handling multi-grained spatial dependency across all modalities. Our models outperform the state-of-the-art with a margin of 4{\%}-16{\%} for most of the metrics on three benchmark datasets.",
}
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%0 Conference Proceedings
%T DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation
%A Mao, Yuzhao
%A Liu, Guang
%A Wang, Xiaojie
%A Gao, Weiguo
%A Li, Xuan
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F mao-etal-2021-dialoguetrm-exploring
%X Emotion dynamics formulates principles explaining the emotional fluctuation during conversations. Recent studies explore the emotion dynamics from the self and inter-personal dependencies, however, ignoring the temporal and spatial dependencies in the situation of multi-modal conversations. To address the issue, we extend the concept of emotion dynamics to multi-modal settings and propose a Dialogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics. Specifically, the intra-modal emotion dynamics is to not only capture the temporal dependency but also satisfy the context preference in every single modality. The inter-modal emotional dynamics aims at handling multi-grained spatial dependency across all modalities. Our models outperform the state-of-the-art with a margin of 4%-16% for most of the metrics on three benchmark datasets.
%R 10.18653/v1/2021.findings-emnlp.229
%U https://aclanthology.org/2021.findings-emnlp.229
%U https://doi.org/10.18653/v1/2021.findings-emnlp.229
%P 2694-2704
Markdown (Informal)
[DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation](https://aclanthology.org/2021.findings-emnlp.229) (Mao et al., Findings 2021)
ACL