Papers › Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine...
Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation
Ru Peng, Yawen Zeng, Junbo Zhao
Past works on multimodal machine translation (MMT) elevate bilingual setup by incorporating additional aligned vision information. However, an image-must requirement of the multimodal dataset largely hinders MMT's development -- namely that it demands an aligned form of [image, source text, target text]. This limitation is generally troublesome during the inference phase especially when the aligned image is not provided as in the normal NMT setup. Thus, in this work, we introduce IKD-MMT, a novel MMT framework to support the image-free inference phase via an inversion knowledge distillation scheme. In particular, a multimodal feature generator is executed with a knowledge distillation module, which directly generates the multimodal feature from (only) source texts as the input. While there have been a few prior works entertaining the possibility to support image-free inference for machine translation, their performances have yet to rival the image-must translation. In our experiments, we identify our method as the first image-free approach to comprehensively rival or even surpass (almost) all image-must frameworks, and achieved the state-of-the-art result on the often-used Multi30k benchmark. Our code and data are available at: https://github.com/pengr/IKD-mmt/tree/master..
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multimodal Machine Translation | Multi30K | IKD-MMT | BLEU (EN-DE) | 41.28 | #2 of 15 | Archive leaderboard | report |
| Multimodal Machine Translation | Multi30K | IKD-MMT | Meteor (EN-DE) | 58.93 | #2 of 15 | Archive leaderboard | report |
| Multimodal Machine Translation | Multi30K | IKD-MMT | Meteor (EN-FR) | 77.20 | #2 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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