Visually Grounded Word Embeddings and Richer Visual Features for Improving Multimodal Neural Machine Translation
This work addresses the challenge of generating accurate translations for image descriptions, which is important for applications in cross-lingual image captioning, but it appears incremental as it builds on existing MNMT methods.
The paper tackled the problem of improving Multimodal Neural Machine Translation (MNMT) by proposing richer visual features from dense captioning models and visually grounded word embeddings, resulting in improved translation quality with concrete gains reported in the abstract.
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 English. This is considered as the multimodal image caption translation task. The images are processed with Convolutional Neural Network (CNN) to extract visual features exploitable by the translation model. So far, the CNNs used are pre-trained on object detection and localization task. We hypothesize that richer architecture, such as dense captioning models, may be more suitable for MNMT and could lead to improved translations. We extend this intuition to the word-embeddings, where we compute both linguistic and visual representation for our corpus vocabulary. We combine and compare different confi