  Our goal is to answer questions about paragraphs describing processes (e.g.,
photosynthesis). Texts of this genre are challenging because the effects of
actions are often implicit (unstated), requiring background knowledge and
inference to reason about the changing world states. To supply this knowledge,
we leverage VerbNet to build a rulebase (called the Semantic Lexicon) of the
preconditions and effects of actions, and use it along with commonsense
knowledge of persistence to answer questions about change. Our evaluation shows
that our system, ProComp, significantly outperforms two strong reading
comprehension (RC) baselines. Our contributions are two-fold: the Semantic
Lexicon rulebase itself, and a demonstration of how a simulation-based approach
to machine reading can outperform RC methods that rely on surface cues alone.
  Since this work was performed, we have developed neural systems that
outperform ProComp, described elsewhere (Dalvi et al., NAACL'18). However, the
Semantic Lexicon remains a novel and potentially useful resource, and its
integration with neural systems remains a currently unexplored opportunity for
further improvements in machine reading about processes.
