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    <abstract>Multimodal machine translation (MMT) integrates visual information to address ambiguity and contextual limitations in neural machine translation (NMT). Some empirical studies have revealed that many MMT models underutilize visual data during translation. They attempt to enhance cross-modal interactions to enable better exploitation of visual data. However, they only focus on simple interactions between nouns in text and corresponding entities in image, overlooking global semantic alignment, particularly for prepositional phrases and verbs in text which are more likely to be translated incorrectly. To address this, we design a Text-Image In-depth Questioning method to deepen interactions and optimize translations. Furthermore, to mitigate errors arising from contextually irrelevant image noise, we propose a Consistency Constraint strategy to improve our approach’s robustness. Our approach achieves state-of-the-art results on five translation directions of Multi30K and AmbigCaps, with +2.35 BLEU on the challenging MSCOCO benchmark, validating our method’s effectiveness in utilizing visual data and capturing comprehensive textual semantics.</abstract>
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%0 Conference Proceedings
%T Multimodal Machine Translation with Text-Image In-depth Questioning
%A Gao, Yue
%A Zhao, Jing
%A Sun, Shiliang
%A Qiao, Xiaosong
%A Song, Tengfei
%A Yang, Hao
%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 gao-etal-2025-multimodal
%X Multimodal machine translation (MMT) integrates visual information to address ambiguity and contextual limitations in neural machine translation (NMT). Some empirical studies have revealed that many MMT models underutilize visual data during translation. They attempt to enhance cross-modal interactions to enable better exploitation of visual data. However, they only focus on simple interactions between nouns in text and corresponding entities in image, overlooking global semantic alignment, particularly for prepositional phrases and verbs in text which are more likely to be translated incorrectly. To address this, we design a Text-Image In-depth Questioning method to deepen interactions and optimize translations. Furthermore, to mitigate errors arising from contextually irrelevant image noise, we propose a Consistency Constraint strategy to improve our approach’s robustness. Our approach achieves state-of-the-art results on five translation directions of Multi30K and AmbigCaps, with +2.35 BLEU on the challenging MSCOCO benchmark, validating our method’s effectiveness in utilizing visual data and capturing comprehensive textual semantics.
%R 10.18653/v1/2025.findings-acl.483
%U https://aclanthology.org/2025.findings-acl.483/
%U https://doi.org/10.18653/v1/2025.findings-acl.483
%P 9274-9287

Markdown (Informal)

[Multimodal Machine Translation with Text-Image In-depth Questioning](https://aclanthology.org/2025.findings-acl.483/) (Gao et al., Findings 2025)

• Multimodal Machine Translation with Text-Image In-depth Questioning (Gao et al., Findings 2025)

ACL

• Yue Gao, Jing Zhao, Shiliang Sun, Xiaosong Qiao, Tengfei Song, and Hao Yang. 2025. Multimodal Machine Translation with Text-Image In-depth Questioning. In Findings of the Association for Computational Linguistics: ACL 2025, pages 9274–9287, Vienna, Austria. Association for Computational Linguistics.