  We introduce the Probabilistic Worldbuilding Model (PWM), a new
fully-symbolic Bayesian model of semantic parsing and reasoning, as a first
step in a research program toward more domain- and task-general NLU and AI.
Humans create internal mental models of their observations which greatly aid in
their ability to understand and reason about a large variety of problems. In
PWM, the meanings of sentences, acquired facts about the world, and
intermediate steps in reasoning are all expressed in a human-readable formal
language, with the design goal of interpretability. PWM is Bayesian, designed
specifically to be able to generalize to new domains and new tasks. We derive
and implement an inference algorithm that reads sentences by parsing and
abducing updates to its latent world model that capture the semantics of those
sentences, and evaluate it on two out-of-domain question-answering datasets:
(1) ProofWriter and (2) a new dataset we call FictionalGeoQA, designed to be
more representative of real language but still simple enough to focus on
evaluating reasoning ability, while being robust against heuristics. Our method
outperforms baselines on both, thereby demonstrating its value as a
proof-of-concept.
