Papers › Doubly-Attentive Decoder for Multi-modal Neural Machine Translation
Doubly-Attentive Decoder for Multi-modal Neural Machine Translation
Iacer Calixto, Qun Liu, Nick Campbell
We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive decoder naturally incorporates spatial visual features obtained using pre-trained convolutional neural networks, bridging the gap between image description and translation. Our decoder learns to attend to source-language words and parts of an image independently by means of two separate attention mechanisms as it generates words in the target language. We find that our model can efficiently exploit not just back-translated in-domain multi-modal data but also large general-domain text-only MT corpora. We also report state-of-the-art results on the Multi30k data set.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multimodal Machine Translation | Multi30K | NMTSRC+IMG | BLEU (EN-DE) | 37.1 | #11 of 15 | Archive leaderboard | report |
| Multimodal Machine Translation | Multi30K | NMTSRC+IMG | Meteor (EN-DE) | 54.5 | #11 of 15 | Archive leaderboard | report |
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