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• W2553522418 abstract "The attention mechanism is an important part of the neural machine translation (NMT) where it was reported to produce richer source representation compared to fixed-length encoding sequence-to-sequence models. Recently, the effectiveness of attention has also been explored in the context of image captioning. In this work, we assess the feasibility of a multimodal attention mechanism that simultaneously focus over an image and its natural language description for generating a description in another language. We train several variants of our proposed attention mechanism on the Multi30k multilingual image captioning dataset. We show that a dedicated attention for each modality achieves up to 1.6 points in BLEU and METEOR compared to a textual NMT baseline." @default.
• W2553522418 created "2016-11-30" @default.
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• W2553522418 date "2016-09-13" @default.
• W2553522418 modified "2023-09-27" @default.
• W2553522418 title "Multimodal Attention for Neural Machine Translation." @default.
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• W2553522418 hasPublicationYear "2016" @default.
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