  Multi-hop Question Answering (QA) is a challenging task since it requires an
accurate aggregation of information from multiple context paragraphs and a
thorough understanding of the underlying reasoning chains. Recent work in
multi-hop QA has shown that performance can be boosted by first decomposing the
questions into simpler, single-hop questions. In this paper, we explore one
additional utility of the multi-hop decomposition from the perspective of
explainable NLP: to create explanation by probing a neural QA model with them.
We hypothesize that in doing so, users will be better able to predict when the
underlying QA system will give the correct answer. Through human participant
studies, we verify that exposing the decomposition probes and answers to the
probes to users can increase their ability to predict system performance on a
question instance basis. We show that decomposition is an effective form of
probing QA systems as well as a promising approach to explanation generation.
In-depth analyses show the need for improvements in decomposition systems.
