Lessons learned in multilingual grounded language learning
Table 6: The Monolingual and joint Multi-translation models trained on translation pairs, and the Multi-comparable trained on the downsampled comparable set with one caption per image.
['[EMPTY]', 'En', 'De', 'Fr', 'Cz']
The Multi-translation models are trained on 29K images paired with a single caption in each language. These models perform better than their Monolingual counterparts, and the German, French, and Czech models are further improved with the c2c objective. The Multi-comparable models are trained by randomly sampling one English and one German caption from the comparable dataset, alongside the French and Czech translation pairs. These models perform as well as the Multi-translation models, and the c2c objective brings further improvements for all languages in this setting.