  An interpretable system for complex, open-domain reasoning needs an
interpretable meaning representation. Natural language is an excellent
candidate -- it is both extremely expressive and easy for humans to understand.
However, manipulating natural language statements in logically consistent ways
is hard. Models have to be precise, yet robust enough to handle variation in
how information is expressed. In this paper, we describe ParaPattern, a method
for building models to generate logical transformations of diverse natural
language inputs without direct human supervision. We use a BART-based model
(Lewis et al., 2020) to generate the result of applying a particular logical
operation to one or more premise statements. Crucially, we have a largely
automated pipeline for scraping and constructing suitable training examples
from Wikipedia, which are then paraphrased to give our models the ability to
handle lexical variation. We evaluate our models using targeted contrast sets
as well as out-of-domain sentence compositions from the QASC dataset (Khot et
al., 2020). Our results demonstrate that our operation models are both accurate
and flexible.
