Harry Potter and the Action Prediction Challenge from Natural Language

David Vilares, Carlos Gómez-Rodríguez


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.
Anthology ID:
N19-1218
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2124–2130
Language:
URL:
https://www.aclweb.org/anthology/N19-1218
DOI:
10.18653/v1/N19-1218
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PDF:
http://aclanthology.lst.uni-saarland.de/N19-1218.pdf
Video:
 https://vimeo.com/355806868