Simulating Action Dynamics with Neural Process Networks
Antoine Bosselut, Omer Levy, Ari Holtzman, Corin Ennis, Dieter Fox, Yejin Choi
Abstract
Understanding procedural language requires anticipating the causal effects of actions, even when they are not explicitly stated. In this work, we introduce Neural Process Networks to understand procedural text through (neural) simulation of action dynamics. Our model complements existing memory architectures with dynamic entity tracking by explicitly modeling actions as state transformers. The model updates the states of the entities by executing learned action operators. Empirical results demonstrate that our proposed model can reason about the unstated causal effects of actions, allowing it to provide more accurate contextual information for understanding and generating procedural text, all while offering more interpretable internal representations than existing alternatives.
BibTeX
@inproceedings{
bosselut2018simulating,
title={Simulating Action Dynamics with Neural Process Networks},
author={Antoine Bosselut and Corin Ennis and Omer Levy and Ari Holtzman and Dieter Fox and Yejin Choi},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=rJYFzMZC-},
}