Multimodal Neural Machine Translation with Search Engine Based Image Retrieval

Recently, numbers of works shows that the performance of neural machine translation (NMT) can be improved to a certain extent with using visual information. However, most of these conclusions are drawn from the analysis of experimental results based on a limited set of bilingual sentence-image pairs, such as Multi30K. In these kinds of datasets, the content of one bilingual parallel sentence pair must be well represented by a manually annotated image, which is different with the actual translation situation. Some previous works are proposed to addressed the problem by retrieving images from exiting sentence-image pairs with topic model. However, because of the limited collection of sentence-image pairs they used, their image retrieval method is difficult to deal with the out-of-vocabulary words, and can hardly prove that visual information enhance NMT rather than the co-occurrence of images and sentences. In this paper, we propose an open-vocabulary image retrieval methods to collect descriptive images for bilingual parallel corpus using image search engine. Next, we propose text-aware attentive visual encoder to filter incorrectly collected noise images. Experiment results on Multi30K and other two translation datasets show that our proposed method achieves significant improvements over strong baselines.