  Neural networks lack the ability to reason about qualitative physics and so
cannot generalize to scenarios and tasks unseen during training. We propose
ESPRIT, a framework for commonsense reasoning about qualitative physics in
natural language that generates interpretable descriptions of physical events.
We use a two-step approach of first identifying the pivotal physical events in
an environment and then generating natural language descriptions of those
events using a data-to-text approach. Our framework learns to generate
explanations of how the physical simulation will causally evolve so that an
agent or a human can easily reason about a solution using those interpretable
descriptions. Human evaluations indicate that ESPRIT produces crucial
fine-grained details and has high coverage of physical concepts compared to
even human annotations. Dataset, code and documentation are available at
https://github.com/salesforce/esprit.
