@inproceedings{ye-etal-2023-rethinkingtmsc,
title = "{R}ethinking{TMSC}: An Empirical Study for Target-Oriented Multimodal Sentiment Classification",
author = "Ye, Junjie and
Zhou, Jie and
Tian, Junfeng and
Wang, Rui and
Zhang, Qi and
Gui, Tao and
Huang, Xuanjing",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.21",
doi = "10.18653/v1/2023.findings-emnlp.21",
pages = "270--277",
abstract = "Recently, Target-oriented Multimodal Sentiment Classification (TMSC) has gained significant attention among scholars. However, current multimodal models have reached a performance bottleneck. To investigate the causes of this problem, we perform extensive empirical evaluation and in-depth analysis of the datasets to answer the following questions: **Q1**: Are the modalities equally important for TMSC? **Q2**: Which multimodal fusion modules are more effective? **Q3**: Do existing datasets adequately support the research? Our experiments and analyses reveal that the current TMSC systems primarily rely on the textual modality, as most of targets{'} sentiments can be determined *solely* by text. Consequently, we point out several directions to work on for the TMSC task in terms of model design and dataset construction. The code and data can be found in https://github.com/Junjie-Ye/RethinkingTMSC.",
}
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<abstract>Recently, Target-oriented Multimodal Sentiment Classification (TMSC) has gained significant attention among scholars. However, current multimodal models have reached a performance bottleneck. To investigate the causes of this problem, we perform extensive empirical evaluation and in-depth analysis of the datasets to answer the following questions: **Q1**: Are the modalities equally important for TMSC? **Q2**: Which multimodal fusion modules are more effective? **Q3**: Do existing datasets adequately support the research? Our experiments and analyses reveal that the current TMSC systems primarily rely on the textual modality, as most of targets’ sentiments can be determined *solely* by text. Consequently, we point out several directions to work on for the TMSC task in terms of model design and dataset construction. The code and data can be found in https://github.com/Junjie-Ye/RethinkingTMSC.</abstract>
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%0 Conference Proceedings
%T RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification
%A Ye, Junjie
%A Zhou, Jie
%A Tian, Junfeng
%A Wang, Rui
%A Zhang, Qi
%A Gui, Tao
%A Huang, Xuanjing
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ye-etal-2023-rethinkingtmsc
%X Recently, Target-oriented Multimodal Sentiment Classification (TMSC) has gained significant attention among scholars. However, current multimodal models have reached a performance bottleneck. To investigate the causes of this problem, we perform extensive empirical evaluation and in-depth analysis of the datasets to answer the following questions: **Q1**: Are the modalities equally important for TMSC? **Q2**: Which multimodal fusion modules are more effective? **Q3**: Do existing datasets adequately support the research? Our experiments and analyses reveal that the current TMSC systems primarily rely on the textual modality, as most of targets’ sentiments can be determined *solely* by text. Consequently, we point out several directions to work on for the TMSC task in terms of model design and dataset construction. The code and data can be found in https://github.com/Junjie-Ye/RethinkingTMSC.
%R 10.18653/v1/2023.findings-emnlp.21
%U https://aclanthology.org/2023.findings-emnlp.21
%U https://doi.org/10.18653/v1/2023.findings-emnlp.21
%P 270-277
Markdown (Informal)
[RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification](https://aclanthology.org/2023.findings-emnlp.21) (Ye et al., Findings 2023)
ACL