# Harry Potter and the Action Prediction Challenge from Natural Language

arXiv: https://arxiv.org/abs/1905.11037 · HTML: https://mlsanity.com/paper/1905.11037/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 2 of 2)

- [Teaching a Massive Open Online Course on Natural Language Processing](https://mlsanity.com/paper/2104.12846/) (2021)
- [Detecting Spells in Fantasy Literature with a Transformer Based Artificial Intelligence](https://mlsanity.com/paper/2308.03660/) (2023)

## Abstract (arXiv)

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.