Multimodal Machine Translation with Text-Image In-depth Questioning (2025.findings-acl)
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| Challenge: | Multimodal machine translation (MMT) models focus on intermodal interactions, but focus on simple interactions between nouns and entities in image, overlooking global semantic alignment. |
| Approach: | They propose a Text-Image In-depth Questioning method to deepen interactions and optimize translations by utilizing visual data to capture global semantic alignment. |
| Outcome: | The proposed method achieves state-of-the-art results on five translation directions of Multi30K and AmbigCaps, with +2.35 BLEU on the challenging MSCOCO benchmark. |
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