Doubly-Attentive Decoder for Multi-modal Neural Machine Translation
This addresses the problem of integrating visual and textual information for translation, which is incremental as it builds on existing attention mechanisms.
The paper tackles multi-modal neural machine translation by introducing a doubly-attentive decoder that independently attends to source-language words and image parts, achieving state-of-the-art results on the Multi30k dataset.
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.