  Prior work on automated question generation has almost exclusively focused on
generating simple questions whose answers can be extracted from a single
document. However, there is an increasing interest in developing systems that
are capable of more complex multi-hop question generation, where answering the
questions requires reasoning over multiple documents. In this work, we
introduce a series of strong transformer models for multi-hop question
generation, including a graph-augmented transformer that leverages relations
between entities in the text. While prior work has emphasized the importance of
graph-based models, we show that we can substantially outperform the
state-of-the-art by 5 BLEU points using a standard transformer architecture. We
further demonstrate that graph-based augmentations can provide complimentary
improvements on top of this foundation. Interestingly, we find that several
important factors--such as the inclusion of an auxiliary contrastive objective
and data filtering could have larger impacts on performance. We hope that our
stronger baselines and analysis provide a constructive foundation for future
work in this area.
