  Learning multi-hop reasoning has been a key challenge for reading
comprehension models, leading to the design of datasets that explicitly focus
on it. Ideally, a model should not be able to perform well on a multi-hop
question answering task without doing multi-hop reasoning. In this paper, we
investigate two recently proposed datasets, WikiHop and HotpotQA. First, we
explore sentence-factored models for these tasks; by design, these models
cannot do multi-hop reasoning, but they are still able to solve a large number
of examples in both datasets. Furthermore, we find spurious correlations in the
unmasked version of WikiHop, which make it easy to achieve high performance
considering only the questions and answers. Finally, we investigate one key
difference between these datasets, namely span-based vs. multiple-choice
formulations of the QA task. Multiple-choice versions of both datasets can be
easily gamed, and two models we examine only marginally exceed a baseline in
this setting. Overall, while these datasets are useful testbeds,
high-performing models may not be learning as much multi-hop reasoning as
previously thought.

Computation and Language;Artificial Intelligence