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Abstract

We explore the challenge of action prediction from textual descriptions of scenes, a testbed to approximate whether text inference can be used to predict upcoming actions. As a case of study, we consider the world of the Harry Potter fantasy novels and inferring what spell will be cast next given a fragment of a story. Spells act as keywords that abstract actions (e.g. 'Alohomora' to open a door) and denote a response to the environment. This idea is used to automatically build HPAC, a corpus containing 82 836 samples and 85 actions. We then evaluate different baselines. Among the tested models, an LSTM-based approach obtains the best performance for frequent actions and large scene descriptions, but approaches such as logistic regression behave well on infrequent actions.

Teaching a Massive Open Online Course on Natural Language Processing

“…labeling coding assignments Text classification assignment is based on the Harry Potter and the Action Prediction Challenge from Natural Language dataset (Vilares and Gómez-Rodríguez, 2019), which uses fiction fantasy texts. Here, the task is the following: given some text preceding a spell occurrence in the text, predict this spell name.…”