  Multi-hop QA with annotated supporting facts, which is the task of reading
comprehension (RC) considering the interpretability of the answer, has been
extensively studied. In this study, we define an interpretable reading
comprehension (IRC) model as a pipeline model with the capability of predicting
unanswerable queries. The IRC model justifies the answer prediction by
establishing consistency between the predicted supporting facts and the actual
rationale for interpretability. The IRC model detects unanswerable questions,
instead of outputting the answer forcibly based on the insufficient
information, to ensure the reliability of the answer. We also propose an
end-to-end training method for the pipeline RC model. To evaluate the
interpretability and the reliability, we conducted the experiments considering
unanswerability in a multi-hop question for a given passage. We show that our
end-to-end trainable pipeline model outperformed a non-interpretable model on
our modified HotpotQA dataset. Experimental results also show that the IRC
model achieves comparable results to the previous non-interpretable models in
spite of the trade-off between prediction performance and interpretability.
