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
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2124–2130
Language:
URL:
https://aclanthology.org/N19-1218
DOI:
10.18653/v1/N19-1218
Bibkey:
Cite (ACL):
David Vilares and Carlos Gómez-Rodríguez. 2019. Harry Potter and the Action Prediction Challenge from Natural Language. In 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), pages 2124–2130, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Harry Potter and the Action Prediction Challenge from Natural Language (Vilares & Gómez-Rodríguez, NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1218.pdf
Video:
 https://aclanthology.org/N19-1218.mp4
Code
 aghie/hpac