  We propose a framework for answering open domain multi-hop questions in which
partial information is read and used to generate followup questions, to finally
be answered by a pretrained single-hop answer extractor. This framework makes
each hop interpretable, and makes the retrieval associated with later hops as
flexible and specific as for the first hop. As a first instantiation of this
framework, we train a pointer-generator network to predict followup questions
based on the question and partial information. This provides a novel
application of a neural question generation network, which is applied to give
weak ground truth single-hop followup questions based on the final answers and
their supporting facts. Learning to generate followup questions that select the
relevant answer spans against downstream supporting facts, while avoiding
distracting premises, poses an exciting semantic challenge for text generation.
We present an evaluation using the two-hop bridge questions of HotpotQA.
