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We address the task of text translation on the How2 dataset using a state ofthe art transformer-based multimodal approach. The question we ask ourselves iswhether visual features can support the translation process, in particular,given that this is a dataset extracted from videos, we focus on the translationof actions, which we believe are poorly captured in current static image-textdatasets currently used for multimodal translation. For that purpose, weextract different types of action features from the videos and carefullyinvestigate how helpful this visual information is by testing whether it canincrease translation quality when used in conjunction with (i) the originaltext and (ii) the original text where action-related words (or all verbs) aremasked out. The latter is a simulation that helps us assess the utility of theimage in cases where the text does not provide enough context about the action,or in the presence of noise in the input text.

Predicting Actions to Help Predict Translations