Papers with MSA tasks

2 papers
Cross-lingual Multimodal Sentiment Analysis for Low-Resource Languages via Language Family Disentanglement and Rethinking Transfer (2025.findings-acl)

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Challenge: Existing multimodal sentiment analysis methods are limited to textual data and cannot handle multimodal scenarios.
Approach: They propose a transfer learning framework that allows cross-lingual and cross-modal alignments and a language family disentanglement module that enhances the sharing of language universals within families.
Outcome: The proposed method is superior to existing methods and can handle low-resource languages.
InfoEnh: Towards Multimodal Sentiment Analysis via Information Bottleneck Filter and Optimal Transport Alignment (2024.lrec-main)

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Challenge: Existing methods for multi-modal sentiment analysis have been developed to overcome these challenges.
Approach: They propose a method that utilizes a masking technique as the bottleneck for information filtering and integrates all modalities into a common feature space via domain adaptation.
Outcome: Extensive experiments on two benchmark MSA datasets show the proposed method performs better than baselines.

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