  An interpretable system for open-domain reasoning needs to express its
reasoning process in a transparent form. Natural language is an attractive
representation for this purpose -- it is both highly expressive and easy for
humans to understand. However, manipulating natural language statements in
logically consistent ways is hard: models must cope with variation in how
meaning is expressed while remaining precise. In this paper, we describe
ParaPattern, a method for building models to generate deductive inferences from
diverse natural language inputs without direct human supervision. We train
BART-based models (Lewis et al., 2020) to generate the result of applying a
particular logical operation to one or more premise statements. Crucially, we
develop a largely automated pipeline for constructing suitable training
examples from Wikipedia. We evaluate our models using out-of-domain sentence
compositions from the QASC (Khot et al., 2020) and EntailmentBank (Dalvi et
al., 2021) datasets as well as targeted perturbation sets. Our results show
that our models are substantially more accurate and flexible than baseline
systems. ParaPattern achieves 85% validity on examples of the 'substitution'
operation from EntailmentBank without the use of any in-domain training data,
matching the performance of a model fine-tuned for EntailmentBank. The full
source code for our method is publicly available.
