@inproceedings{zeng-etal-2022-mitigating,
title = "Mitigating Inconsistencies in Multimodal Sentiment Analysis under Uncertain Missing Modalities",
author = "Zeng, Jiandian and
Zhou, Jiantao and
Liu, Tianyi",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.189/",
doi = "10.18653/v1/2022.emnlp-main.189",
pages = "2924--2934",
abstract = "For the missing modality problem in Multimodal Sentiment Analysis (MSA), the inconsistency phenomenon occurs when the sentiment changes due to the absence of a modality. The absent modality that determines the overall semantic can be considered as a key missing modality. However, previous works all ignored the inconsistency phenomenon, simply discarding missing modalities or solely generating associated features from available modalities. The neglect of the key missing modality case may lead to incorrect semantic results. To tackle the issue, we propose an Ensemble-based Missing Modality Reconstruction (EMMR) network to detect and recover semantic features of the key missing modality. Specifically, we first learn joint representations with remaining modalities via a backbone encoder-decoder network. Then, based on the recovered features, we check the semantic consistency to determine whether the absent modality is crucial to the overall sentiment polarity. Once the inconsistency problem due to the key missing modality exists, we integrate several encoder-decoder approaches for better decision making. Extensive experiments and analyses are conducted on CMU-MOSI and IEMOCAP datasets, validating the superiority of the proposed method."
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<abstract>For the missing modality problem in Multimodal Sentiment Analysis (MSA), the inconsistency phenomenon occurs when the sentiment changes due to the absence of a modality. The absent modality that determines the overall semantic can be considered as a key missing modality. However, previous works all ignored the inconsistency phenomenon, simply discarding missing modalities or solely generating associated features from available modalities. The neglect of the key missing modality case may lead to incorrect semantic results. To tackle the issue, we propose an Ensemble-based Missing Modality Reconstruction (EMMR) network to detect and recover semantic features of the key missing modality. Specifically, we first learn joint representations with remaining modalities via a backbone encoder-decoder network. Then, based on the recovered features, we check the semantic consistency to determine whether the absent modality is crucial to the overall sentiment polarity. Once the inconsistency problem due to the key missing modality exists, we integrate several encoder-decoder approaches for better decision making. Extensive experiments and analyses are conducted on CMU-MOSI and IEMOCAP datasets, validating the superiority of the proposed method.</abstract>
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%0 Conference Proceedings
%T Mitigating Inconsistencies in Multimodal Sentiment Analysis under Uncertain Missing Modalities
%A Zeng, Jiandian
%A Zhou, Jiantao
%A Liu, Tianyi
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zeng-etal-2022-mitigating
%X For the missing modality problem in Multimodal Sentiment Analysis (MSA), the inconsistency phenomenon occurs when the sentiment changes due to the absence of a modality. The absent modality that determines the overall semantic can be considered as a key missing modality. However, previous works all ignored the inconsistency phenomenon, simply discarding missing modalities or solely generating associated features from available modalities. The neglect of the key missing modality case may lead to incorrect semantic results. To tackle the issue, we propose an Ensemble-based Missing Modality Reconstruction (EMMR) network to detect and recover semantic features of the key missing modality. Specifically, we first learn joint representations with remaining modalities via a backbone encoder-decoder network. Then, based on the recovered features, we check the semantic consistency to determine whether the absent modality is crucial to the overall sentiment polarity. Once the inconsistency problem due to the key missing modality exists, we integrate several encoder-decoder approaches for better decision making. Extensive experiments and analyses are conducted on CMU-MOSI and IEMOCAP datasets, validating the superiority of the proposed method.
%R 10.18653/v1/2022.emnlp-main.189
%U https://aclanthology.org/2022.emnlp-main.189/
%U https://doi.org/10.18653/v1/2022.emnlp-main.189
%P 2924-2934
Markdown (Informal)
[Mitigating Inconsistencies in Multimodal Sentiment Analysis under Uncertain Missing Modalities](https://aclanthology.org/2022.emnlp-main.189/) (Zeng et al., EMNLP 2022)
ACL