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    <abstract>Existing multimodal sentiment analysis (MSA) methods have achieved significant success, leveraging cross-modal large-scale models (LLMs) and extensive pre-training data. However, these methods struggle to handle MSA tasks in low-resource languages. While multilingual LLMs enable cross-lingual transfer, they are limited to textual data and cannot address multimodal scenarios. To achieve MSA in low-resource languages, we propose a novel transfer learning framework named Language Family Disentanglement and Rethinking Transfer (LFD-RT). During pre-training, we establish cross-lingual and cross-modal alignments, followed by a language family disentanglement module that enhances the sharing of language universals within families while reducing noise from cross-family alignments. We propose a rethinking strategy for unsupervised fine-tuning that adapts the pre-trained model to MSA tasks in low-resource languages. Experimental results demonstrate the superiority of our method and its strong language-transfer capability on target low-resource languages. We commit to making our code and data publicly available, and the access link will be provided here.</abstract>
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%0 Conference Proceedings
%T Cross-lingual Multimodal Sentiment Analysis for Low-Resource Languages via Language Family Disentanglement and Rethinking Transfer
%A Chen, Long
%A Guan, Shuoyu
%A Huang, Xiaohua
%A Wang, Wen-Jing
%A Xu, Cai
%A Guan, Ziyu
%A Zhao, Wei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F chen-etal-2025-cross
%X Existing multimodal sentiment analysis (MSA) methods have achieved significant success, leveraging cross-modal large-scale models (LLMs) and extensive pre-training data. However, these methods struggle to handle MSA tasks in low-resource languages. While multilingual LLMs enable cross-lingual transfer, they are limited to textual data and cannot address multimodal scenarios. To achieve MSA in low-resource languages, we propose a novel transfer learning framework named Language Family Disentanglement and Rethinking Transfer (LFD-RT). During pre-training, we establish cross-lingual and cross-modal alignments, followed by a language family disentanglement module that enhances the sharing of language universals within families while reducing noise from cross-family alignments. We propose a rethinking strategy for unsupervised fine-tuning that adapts the pre-trained model to MSA tasks in low-resource languages. Experimental results demonstrate the superiority of our method and its strong language-transfer capability on target low-resource languages. We commit to making our code and data publicly available, and the access link will be provided here.
%R 10.18653/v1/2025.findings-acl.338
%U https://aclanthology.org/2025.findings-acl.338/
%U https://doi.org/10.18653/v1/2025.findings-acl.338
%P 6513-6522

Markdown (Informal)

[Cross-lingual Multimodal Sentiment Analysis for Low-Resource Languages via Language Family Disentanglement and Rethinking Transfer](https://aclanthology.org/2025.findings-acl.338/) (Chen et al., Findings 2025)

• Cross-lingual Multimodal Sentiment Analysis for Low-Resource Languages via Language Family Disentanglement and Rethinking Transfer (Chen et al., Findings 2025)

ACL

• Long Chen, Shuoyu Guan, Xiaohua Huang, Wen-Jing Wang, Cai Xu, Ziyu Guan, and Wei Zhao. 2025. Cross-lingual Multimodal Sentiment Analysis for Low-Resource Languages via Language Family Disentanglement and Rethinking Transfer. In Findings of the Association for Computational Linguistics: ACL 2025, pages 6513–6522, Vienna, Austria. Association for Computational Linguistics.