Papers › Gumbel-Attention for Multi-modal Machine Translation
Gumbel-Attention for Multi-modal Machine Translation
Pengbo Liu, Hailong Cao, Tiejun Zhao
Multi-modal machine translation (MMT) improves translation quality by introducing visual information. However, the existing MMT model ignores the problem that the image will bring information irrelevant to the text, causing much noise to the model and affecting the translation quality. This paper proposes a novel Gumbel-Attention for multi-modal machine translation, which selects the text-related parts of the image features. Specifically, different from the previous attention-based method, we first use a differentiable method to select the image information and automatically remove the useless parts of the image features. Experiments prove that our method retains the image features related to the text, and the remaining parts help the MMT model generates better translations.
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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 | Gumbel-Attention MMT | BLEU (EN-DE) | 39.2 | #5 of 15 | Archive leaderboard | report |
| Multimodal Machine Translation | Multi30K | Gumbel-Attention MMT | Meteor (EN-DE) | 57.8 | #5 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.
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