Learning the Peculiar Value of Actions

We consider the task of automatically estimating the value of human actions. We cast the problem as a supervised learningto-rank problem between pairs of action descriptions. We present a large, novel data set for this task which consists of challenges from the I Will If You Will Earth Hour challenge. We show that an SVM ranking model with simple linguistic features can accurately predict the relative value of actions.