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            <title>Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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    <abstract>Document Image Machine Translation (DIMT) aims to translate text within document images, facing generalization challenges due to limited training data and the complex interplay between visual and textual information. To address these challenges, we introduce M4Doc, a novel single-to-mix Modality alignment framework leveraging Multimodal Large Language Models (MLLMs). M4Doc aligns an imageonly encoder with the multimodal representations of an MLLM, pre-trained on large-scale document image datasets. This alignment enables a lightweight DIMT model to learn crucial visual-textual correlations during training. During inference, M4Doc bypasses the MLLM, maintaining computational efficiency while benefiting from its multimodal knowledge. Comprehensive experiments demonstrate substantial improvements in translation quality, especially in cross-domain generalization and challenging document image scenarios. The code will be released upon acceptance.</abstract>
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%0 Conference Proceedings
%T Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation
%A Liang, Yupu
%A Zhang, Yaping
%A Zhang, Zhiyang
%A Zhao, Yang
%A Xiang, Lu
%A Zong, Chengqing
%A Zhou, Yu
%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 liang-etal-2025-single
%X Document Image Machine Translation (DIMT) aims to translate text within document images, facing generalization challenges due to limited training data and the complex interplay between visual and textual information. To address these challenges, we introduce M4Doc, a novel single-to-mix Modality alignment framework leveraging Multimodal Large Language Models (MLLMs). M4Doc aligns an imageonly encoder with the multimodal representations of an MLLM, pre-trained on large-scale document image datasets. This alignment enables a lightweight DIMT model to learn crucial visual-textual correlations during training. During inference, M4Doc bypasses the MLLM, maintaining computational efficiency while benefiting from its multimodal knowledge. Comprehensive experiments demonstrate substantial improvements in translation quality, especially in cross-domain generalization and challenging document image scenarios. The code will be released upon acceptance.
%R 10.18653/v1/2025.acl-long.606
%U https://aclanthology.org/2025.acl-long.606/
%U https://doi.org/10.18653/v1/2025.acl-long.606
%P 12391-12408

Markdown (Informal)

[Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation](https://aclanthology.org/2025.acl-long.606/) (Liang et al., ACL 2025)

• Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation (Liang et al., ACL 2025)

ACL

• Yupu Liang, Yaping Zhang, Zhiyang Zhang, Yang Zhao, Lu Xiang, Chengqing Zong, and Yu Zhou. 2025. Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12391–12408, Vienna, Austria. Association for Computational Linguistics.