@inproceedings{zhang-etal-2020-multi,
title = "Multi-modal Multi-label Emotion Detection with Modality and Label Dependence",
author = "Zhang, Dong and
Ju, Xincheng and
Li, Junhui and
Li, Shoushan and
Zhu, Qiaoming and
Zhou, Guodong",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.291/",
doi = "10.18653/v1/2020.emnlp-main.291",
pages = "3584--3593",
abstract = "As an important research issue in the natural language processing community, multi-label emotion detection has been drawing more and more attention in the last few years. However, almost all existing studies focus on one modality (e.g., textual modality). In this paper, we focus on multi-label emotion detection in a multi-modal scenario. In this scenario, we need to consider both the dependence among different labels (label dependence) and the dependence between each predicting label and different modalities (modality dependence). Particularly, we propose a multi-modal sequence-to-set approach to effectively model both kinds of dependence in multi-modal multi-label emotion detection. The detailed evaluation demonstrates the effectiveness of our approach."
}
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<abstract>As an important research issue in the natural language processing community, multi-label emotion detection has been drawing more and more attention in the last few years. However, almost all existing studies focus on one modality (e.g., textual modality). In this paper, we focus on multi-label emotion detection in a multi-modal scenario. In this scenario, we need to consider both the dependence among different labels (label dependence) and the dependence between each predicting label and different modalities (modality dependence). Particularly, we propose a multi-modal sequence-to-set approach to effectively model both kinds of dependence in multi-modal multi-label emotion detection. The detailed evaluation demonstrates the effectiveness of our approach.</abstract>
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%0 Conference Proceedings
%T Multi-modal Multi-label Emotion Detection with Modality and Label Dependence
%A Zhang, Dong
%A Ju, Xincheng
%A Li, Junhui
%A Li, Shoushan
%A Zhu, Qiaoming
%A Zhou, Guodong
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-multi
%X As an important research issue in the natural language processing community, multi-label emotion detection has been drawing more and more attention in the last few years. However, almost all existing studies focus on one modality (e.g., textual modality). In this paper, we focus on multi-label emotion detection in a multi-modal scenario. In this scenario, we need to consider both the dependence among different labels (label dependence) and the dependence between each predicting label and different modalities (modality dependence). Particularly, we propose a multi-modal sequence-to-set approach to effectively model both kinds of dependence in multi-modal multi-label emotion detection. The detailed evaluation demonstrates the effectiveness of our approach.
%R 10.18653/v1/2020.emnlp-main.291
%U https://aclanthology.org/2020.emnlp-main.291/
%U https://doi.org/10.18653/v1/2020.emnlp-main.291
%P 3584-3593
Markdown (Informal)
[Multi-modal Multi-label Emotion Detection with Modality and Label Dependence](https://aclanthology.org/2020.emnlp-main.291/) (Zhang et al., EMNLP 2020)
ACL