  Reasoning about information from multiple parts of a passage to derive an
answer is an open challenge for reading-comprehension models. In this paper, we
present an approach that reasons about complex questions by decomposing them to
simpler subquestions that can take advantage of single-span extraction
reading-comprehension models, and derives the final answer according to
instructions in a predefined reasoning template. We focus on subtraction-based
arithmetic questions and evaluate our approach on a subset of the DROP dataset.
We show that our approach is competitive with the state-of-the-art while being
interpretable and requires little supervision
