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        <title>GIIFT: Graph-guided Inductive Image-free Multimodal Machine Translation</title>
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    <abstract>Multimodal Machine Translation (MMT) has demonstrated the significant help of visual information in machine translation. However, existing MMT methods face challenges in leveraging the modality gap by enforcing rigid visual-linguistic alignment whilst being confined to inference within their trained multimodal domains. In this work, we construct novel multimodal scene graphs to preserve and integrate modality-specific information and introduce GIIFT, a two-stage Graph-guided Inductive Image-Free MMT framework that uses a cross-modal Graph Attention Network adapter to learn multimodal knowledge in a unified fused space and inductively generalize it to broader image-free translation domains. Experimental results on the Multi30K dataset of English-to-French and English-to-German tasks demonstrate that our GIIFT surpasses existing approaches and achieves the state-of-the-art, even without images during inference. Results on the WMT benchmark show significant improvements over the image-free translation baselines, demonstrating the strength of GIIFT towards inductive image-free inference.</abstract>
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%0 Conference Proceedings
%T GIIFT: Graph-guided Inductive Image-free Multimodal Machine Translation
%A Xiong, Jiafeng
%A Zhao, Yuting
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Tenth Conference on Machine Translation
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-341-8
%F xiong-zhao-2025-giift
%X Multimodal Machine Translation (MMT) has demonstrated the significant help of visual information in machine translation. However, existing MMT methods face challenges in leveraging the modality gap by enforcing rigid visual-linguistic alignment whilst being confined to inference within their trained multimodal domains. In this work, we construct novel multimodal scene graphs to preserve and integrate modality-specific information and introduce GIIFT, a two-stage Graph-guided Inductive Image-Free MMT framework that uses a cross-modal Graph Attention Network adapter to learn multimodal knowledge in a unified fused space and inductively generalize it to broader image-free translation domains. Experimental results on the Multi30K dataset of English-to-French and English-to-German tasks demonstrate that our GIIFT surpasses existing approaches and achieves the state-of-the-art, even without images during inference. Results on the WMT benchmark show significant improvements over the image-free translation baselines, demonstrating the strength of GIIFT towards inductive image-free inference.
%U https://aclanthology.org/2025.wmt-1.6/
%P 98-112

Markdown (Informal)

[GIIFT: Graph-guided Inductive Image-free Multimodal Machine Translation](https://aclanthology.org/2025.wmt-1.6/) (Xiong & Zhao, WMT 2025)

• GIIFT: Graph-guided Inductive Image-free Multimodal Machine Translation (Xiong & Zhao, WMT 2025)

ACL

• Jiafeng Xiong and Yuting Zhao. 2025. GIIFT: Graph-guided Inductive Image-free Multimodal Machine Translation. In Proceedings of the Tenth Conference on Machine Translation, pages 98–112, Suzhou, China. Association for Computational Linguistics.