From Words to Sentences: A Progressive Learning Approach for Zero-resource Machine Translation with Visual Pivots
Table 1: Data splits for the IAPR-TC12 and Multi30k datasets.
['Pairs', 'Train I-En', 'Train I-De', 'Val I-En', 'Val I-De', 'Test En-De']
We utilize two benchmark image-pivot translation datasets IAPR-TC12 and Multi30k. We randomly select 18K images for training, 1K for validation and 1K for testing. Since in the realistic image-pivot setting, images in different languages are mostly non-overlapped, we randomly split the training and validation set into two parts of equal size. One part is constructed only with image-English pairs and the other only with image-German pairs. We utilize the former task annotation, where each image is annotated with one English description and its German translation. We also apply the similar non-overlapping split operation as in IAPR-TC12 to simulate the non-overlap setting.