  We present a methodological framework for inferring symmetry of verb
predicates in natural language. Empirical work on predicate symmetry has taken
two main approaches. The feature-based approach focuses on linguistic features
pertaining to symmetry. The context-based approach denies the existence of
absolute symmetry but instead argues that such inference is context dependent.
We develop methods that formalize these approaches and evaluate them against a
novel symmetry inference sentence (SIS) dataset comprised of 400 naturalistic
usages of literature-informed verbs spanning the spectrum of
symmetry-asymmetry. Our results show that a hybrid transfer learning model that
integrates linguistic features with contextualized language models most
faithfully predicts the empirical data. Our work integrates existing approaches
to symmetry in natural language and suggests how symmetry inference can improve
systematicity in state-of-the-art language models.
