  We propose a new model, DocHopper, that iteratively attends to different
parts of long, hierarchically structured documents to answer complex questions.
Similar to multi-hop question-answering (QA) systems, at each step, DocHopper
uses a query $q$ to attend to information from a document, combines this
``retrieved'' information with $q$ to produce the next query. However, in
contrast to most previous multi-hop QA systems, DocHopper is able to
``retrieve'' either short passages or long sections of the document, thus
emulating a multi-step process of ``navigating'' through a long document to
answer a question. To enable this novel behavior, DocHopper does not combine
document information with $q$ by concatenating text to the text of $q$, but by
combining a compact neural representation of $q$ with a compact neural
representation of a hierarchical part of the document, which can potentially be
quite large. We experiment with DocHopper on four different QA tasks that
require reading long and complex documents to answer multi-hop questions, and
show that DocHopper achieves state-of-the-art results on three of the datasets.
Additionally, DocHopper is efficient at inference time, being 3--10 times
faster than the baselines.

Computation and Language;Artificial Intelligence