Before revision:  These sub-questions are posed to different existing QA models and , together with their answers , provide a natural language explanation of the exact reasoning used by the model. We present the first system, incorporating a neural factoid QA model and a symbolic calculator , that uses decomposition for the DROP dataset, while also generalizing to the multi-hop HotpotQA dataset.
After revision:  These sub-questions are posed to different existing QA models and , together with their answers , provide a natural language explanation of the model's reasoning. We use this framework to build ModularQA, a system that can answer multi-hop reasoning questions by decomposing them into sub-questions answerable by a neural factoid single-span QA model and a symbolic calculator , that uses decomposition for the DROP dataset, while also generalizing to the multi-hop HotpotQA dataset.