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Multi-hop Question Answering (QA) is a challenging task since it requires anaccurate aggregation of information from multiple context paragraphs and athorough understanding of the underlying reasoning chains. Recent work inmulti-hop QA has shown that performance can be boosted by first decomposing thequestions into simpler, single-hop questions. In this paper, we explore oneadditional utility of the multi-hop decomposition from the perspective ofexplainable 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 theunderlying QA system will give the correct answer. Through human participantstudies, we verify that exposing the decomposition probes and answers to theprobes to users can increase their ability to predict system performance on aquestion instance basis. We show that decomposition is an effective form ofprobing QA systems as well as a promising approach to explanation generation.In-depth analyses show the need for improvements in decomposition systems.

Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes