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We explore the challenge of action prediction from textual descriptions ofscenes, a testbed to approximate whether text inference can be used to predictupcoming actions. As a case of study, we consider the world of the Harry Potterfantasy novels and inferring what spell will be cast next given a fragment of astory. Spells act as keywords that abstract actions (e.g. 'Alohomora' to open adoor) and denote a response to the environment. This idea is used toautomatically build HPAC, a corpus containing 82,836 samples and 85 actions. Wethen evaluate different baselines. Among the tested models, an LSTM-basedapproach obtains the best performance for frequent actions and large scenedescriptions, but approaches such as logistic regression behave well oninfrequent actions.

Harry Potter and the Action Prediction Challenge from Natural Language