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    <abstract>Multimodal Sentiment Analysis (MSA) is a rapidly developing field that integrates multimodal information to recognize sentiments, and existing models have made significant progress in this area. The central challenge in MSA is multimodal fusion, which is predominantly addressed by Multimodal Transformers (MulTs). Although act as the paradigm, MulTs suffer from efficiency concerns. In this work, from the perspective of efficiency optimization, we propose and prove that MulTs are hierarchical modal-wise heterogeneous graphs (HMHGs), and we introduce the graph-structured representation pattern of MulTs. Based on this pattern, we propose an Interlaced Mask (IM) mechanism to design the Graph-Structured and Interlaced-Masked Multimodal Transformer (GsiT). It is formally equivalent to MulTs which achieves an efficient weight-sharing mechanism without information disorder through IM, enabling All-Modal-In-One fusion with only 1/3 of the parameters of pure MulTs. A kernel called Decomposition is implemented to ensure avoiding additional computational overhead. Moreover, it achieves significantly higher performance than traditional MulTs. To further validate the effectiveness of GsiT itself and the HMHG concept, we integrate them into multiple state-of-the-art models and demonstrate notable performance improvements and parameter reduction on widely used MSA datasets. Experimental results also demonstrate its effectiveness on other multimodal tasks. The code is available in https://github.com/drewjin/GsiT.git.</abstract>
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%0 Conference Proceedings
%T Multimodal Transformers are Hierarchical Modal-wise Heterogeneous Graphs
%A Jin, Yijie
%A Peng, Junjie
%A Lin, Xuanchao
%A Yuan, Haochen
%A Wang, Lan
%A Zheng, Cangzhi
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F jin-etal-2025-multimodal
%X Multimodal Sentiment Analysis (MSA) is a rapidly developing field that integrates multimodal information to recognize sentiments, and existing models have made significant progress in this area. The central challenge in MSA is multimodal fusion, which is predominantly addressed by Multimodal Transformers (MulTs). Although act as the paradigm, MulTs suffer from efficiency concerns. In this work, from the perspective of efficiency optimization, we propose and prove that MulTs are hierarchical modal-wise heterogeneous graphs (HMHGs), and we introduce the graph-structured representation pattern of MulTs. Based on this pattern, we propose an Interlaced Mask (IM) mechanism to design the Graph-Structured and Interlaced-Masked Multimodal Transformer (GsiT). It is formally equivalent to MulTs which achieves an efficient weight-sharing mechanism without information disorder through IM, enabling All-Modal-In-One fusion with only 1/3 of the parameters of pure MulTs. A kernel called Decomposition is implemented to ensure avoiding additional computational overhead. Moreover, it achieves significantly higher performance than traditional MulTs. To further validate the effectiveness of GsiT itself and the HMHG concept, we integrate them into multiple state-of-the-art models and demonstrate notable performance improvements and parameter reduction on widely used MSA datasets. Experimental results also demonstrate its effectiveness on other multimodal tasks. The code is available in https://github.com/drewjin/GsiT.git.
%R 10.18653/v1/2025.acl-long.109
%U https://aclanthology.org/2025.acl-long.109/
%U https://doi.org/10.18653/v1/2025.acl-long.109
%P 2188-2209

Markdown (Informal)

[Multimodal Transformers are Hierarchical Modal-wise Heterogeneous Graphs](https://aclanthology.org/2025.acl-long.109/) (Jin et al., ACL 2025)

• Multimodal Transformers are Hierarchical Modal-wise Heterogeneous Graphs (Jin et al., ACL 2025)

ACL

• Yijie Jin, Junjie Peng, Xuanchao Lin, Haochen Yuan, Lan Wang, and Cangzhi Zheng. 2025. Multimodal Transformers are Hierarchical Modal-wise Heterogeneous Graphs. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2188–2209, Vienna, Austria. Association for Computational Linguistics.