  Recently, models have been shown to predict the effects of unexpected
situations, e.g., would cloudy skies help or hinder plant growth? Given a
context, the goal of such situational reasoning is to elicit the consequences
of a new situation (st) that arises in that context. We propose a method to
iteratively build a graph of relevant consequences explicitly in a structured
situational graph (st-graph) using natural language queries over a finetuned
language model (M). Across multiple domains, CURIE generates st-graphs that
humans find relevant and meaningful in eliciting the consequences of a new
situation. We show that st-graphs generated by CURIE improve a situational
reasoning end task (WIQA-QA) by 3 points on accuracy by simply augmenting their
input with our generated situational graphs, especially for a hard subset that
requires background knowledge and multi-hop reasoning.
