Papers with ZeroMMT
Towards Zero-Shot Multimodal Machine Translation (2025.findings-naacl)
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| Challenge: | Current multimodal machine translation systems rely on fully supervised data, which is costly to collect and prevents extension of MMT to language pairs with no such data. |
| Approach: | They propose a method to bypass the need for fully supervised data to train MMT systems . they adapt a strong text-only machine translation model to a visually conditioned language model and a divergence test set to evaluate how well models use images to disambiguate translations. |
| Outcome: | The proposed method can generalize to languages with no fully supervised training data. |