  We propose a general framework called Text Modular Networks(TMNs) for
building interpretable systems that learn to solve complex tasks by decomposing
them into simpler ones solvable by existing models. To ensure solvability of
simpler tasks, TMNs learn the textual input-output behavior (i.e., language) of
existing models through their datasets. This differs from prior
decomposition-based approaches which, besides being designed specifically for
each complex task, produce decompositions independent of existing sub-models.
Specifically, we focus on Question Answering (QA) and show how to train a
next-question generator to sequentially produce sub-questions targeting
appropriate sub-models, without additional human annotation. These
sub-questions and answers provide a faithful 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.
Our experiments show that ModularQA is more versatile than existing explainable
systems for DROP and HotpotQA datasets, is more robust than state-of-the-art
blackbox (uninterpretable) systems, and generates more understandable and
trustworthy explanations compared to prior work.
