  Humans understand language based on the rich background knowledge about how
the physical world works, which in turn allows us to reason about the physical
world through language. In addition to the properties of objects (e.g., boats
require fuel) and their affordances, i.e., the actions that are applicable to
them (e.g., boats can be driven), we can also reason about if-then inferences
between what properties of objects imply the kind of actions that are
applicable to them (e.g., that if we can drive something then it likely
requires fuel).
  In this paper, we investigate the extent to which state-of-the-art neural
language representations, trained on a vast amount of natural language text,
demonstrate physical commonsense reasoning. While recent advancements of neural
language models have demonstrated strong performance on various types of
natural language inference tasks, our study based on a dataset of over 200k
newly collected annotations suggests that neural language representations still
only learn associations that are explicitly written down.
