Does Multimodality Help Human and Machine for Translation and Image Captioning?
Table 1: Training Data for Task 1.
['Side', 'Vocabulary', 'Words']
This dataset consists of 29K parallel sentences (direct translations of image descriptions from English to German) for training, 1014 for validation and finally 1000 for the test set. We preprocessed the dataset using the punctuation normalization, tokenization and lowercasing scripts from Moses. This reduces the target vocabulary from 18670 to 15820 unique tokens. During translation generation, the splitted compounds are stitched back together.