  A key challenge of multi-hop question answering (QA) in the open-domain
setting is to accurately retrieve the supporting passages from a large corpus.
Existing work on open-domain QA typically relies on off-the-shelf information
retrieval (IR) techniques to retrieve \textbf{answer passages}, i.e., the
passages containing the groundtruth answers. However, IR-based approaches are
insufficient for multi-hop questions, as the topic of the second or further
hops is not explicitly covered by the question. To resolve this issue, we
introduce a new sub-problem of open-domain multi-hop QA, which aims to
recognize the bridge (\emph{i.e.}, the anchor that links to the answer passage)
from the context of a set of start passages with a reading comprehension model.
This model, the \textbf{bridge reasoner}, is trained with a weakly supervised
signal and produces the candidate answer passages for the \textbf{passage
reader} to extract the answer. On the full-wiki HotpotQA benchmark, we
significantly improve the baseline method by 14 point F1. Without using any
memory-inefficient contextual embeddings, our result is also competitive with
the state-of-the-art that applies BERT in multiple modules.
