Distilling Translations with Visual Awareness
Table 5: Results of human annotation of blanked translations (English-German). We report counts of blanks resolved by each system, as well as total source blank count for each selection (50 sentences selected randomly).
['setup', 'base', 'del', 'del+obj', 'gold']
As expected, they show that the RND and AMB blanks are more difficult to resolve (at most 40% resolved as compared to 61% for PERS). Translations of the majority of those blanks tend to be guessed by the textual context alone (especially for verbs). Image information is more helpful for PERS: we observe an increase of 10% in resolved blanks for del+obj as compared to del. However, for PERS the textual context is still enough in the majority of the cases: models tend to associate men with sports or women with cooking and are usually right (see