  In this paper, we investigate whether symbolic semantic representations,
extracted from deep semantic parsers, can help reasoning over the states of
involved entities in a procedural text. We consider a deep semantic
parser~(TRIPS) and semantic role labeling as two sources of semantic parsing
knowledge. First, we propose PROPOLIS, a symbolic parsing-based procedural
reasoning framework. Second, we integrate semantic parsing information into
state-of-the-art neural models to conduct procedural reasoning. Our experiments
indicate that explicitly incorporating such semantic knowledge improves
procedural understanding. This paper presents new metrics for evaluating
procedural reasoning tasks that clarify the challenges and identify differences
among neural, symbolic, and integrated models.
