  This technical report describes the EgoTask Translation approach that
explores relations among a set of egocentric video tasks in the Ego4D
challenge. To improve the primary task of interest, we propose to leverage
existing models developed for other related tasks and design a task translator
that learns to ''translate'' auxiliary task features to the primary task. With
no modification to the baseline architectures, our proposed approach achieves
competitive performance on two Ego4D challenges, ranking the 1st in the talking
to me challenge and the 3rd in the PNR keyframe localization challenge.
