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Modulating and attending the source image during encoding improves Multimodal Translation
Jean-Benoit Delbrouck, Stéphane Dupont
We propose a new and fully end-to-end approach for multimodal translation where the source text encoder modulates the entire visual input processing using conditional batch normali…
An empirical study on the effectiveness of images in Multimodal Neural Machine Translation
Jean-Benoit Delbrouck, Stéphane Dupont
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 f…
Visually Grounded Word Embeddings and Richer Visual Features for Improving Multimodal Neural Machine Translation
Jean-Benoit Delbrouck, Stéphane Dupont, Omar Seddati
In Multimodal Neural Machine Translation (MNMT), a neural model generates a translated sentence that describes an image, given the image itself and one source descriptions in Engli…
Multimodal Compact Bilinear Pooling for Multimodal Neural Machine Translation
Jean-Benoit Delbrouck, Stephane Dupont
In state-of-the-art Neural Machine Translation, an attention mechanism is used during decoding to enhance the translation. At every step, the decoder uses this mechanism to focus o…