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We present a doubly-attentive multimodal machine translation model.Our model learns to attend to source language and spatial-preserving CONV 5,4 visual features as separate attention mechanisms in a neural translation model.In image description translation experiments (Task 1), we find an improvement of 2.3 Meteor points compared to initialising the hidden state of the decoder with only the FC 7 features and 2.9 Meteor points compared to a text-only neural machine translation baseline, confirming the useful nature of attending to the CONV 5,4 features.
Calixto et al. (Fri,) studied this question.
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