  Retrieving information from correlative paragraphs or documents to answer
open-domain multi-hop questions is very challenging. To deal with this
challenge, most of the existing works consider paragraphs as nodes in a graph
and propose graph-based methods to retrieve them. However, in this paper, we
point out the intrinsic defect of such methods. Instead, we propose a new
architecture that models paragraphs as sequential data and considers multi-hop
information retrieval as a kind of sequence labeling task. Specifically, we
design a rewritable external memory to model the dependency among paragraphs.
Moreover, a threshold gate mechanism is proposed to eliminate the distraction
of noise paragraphs. We evaluate our method on both full wiki and distractor
subtask of HotpotQA, a public textual multi-hop QA dataset requiring multi-hop
information retrieval. Experiments show that our method achieves significant
improvement over the published state-of-the-art method in retrieval and
downstream QA task performance.
