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• W2726413184 abstract "In state-of-the-art Neural Machine Translation (NMT), an attention mechanism is used during decoding to enhance the translation. At every step, the decoder uses this mechanism to focus on different parts of the source sentence to gather the most useful information before outputting its target word. Recently, the effectiveness of the attention mechanism has also been explored for multi-modal tasks, where it becomes possible to focus both on sentence parts and image regions that they describe. In this paper, we compare several attention mechanism on the multi-modal translation task (English, image → German) and evaluate the ability of the model to make use of images to improve translation. We surpass state-of-the-art scores on the Multi30k data set, we nevertheless identify and report different misbehavior of the machine while translating." @default.
• W2726413184 created "2017-07-14" @default.
• W2726413184 creator A5005041960 @default.
• W2726413184 creator A5083563222 @default.
• W2726413184 date "2017-01-01" @default.
• W2726413184 modified "2023-10-17" @default.
• W2726413184 title "An empirical study on the effectiveness of images in Multimodal Neural Machine Translation" @default.
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• W2726413184 doi "https://doi.org/10.18653/v1/d17-1095" @default.
• W2726413184 hasPublicationYear "2017" @default.
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