  End-to-end neural models have made significant progress in question
answering, however recent studies show that these models implicitly assume that
the answer and evidence appear close together in a single document. In this
work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new
question answering model that combines information from evidence across
multiple documents. The CFC consists of a coarse-grain module that interprets
documents with respect to the query then finds a relevant answer, and a
fine-grain module which scores each candidate answer by comparing its
occurrences across all of the documents with the query. We design these modules
using hierarchies of coattention and self-attention, which learn to emphasize
different parts of the input. On the Qangaroo WikiHop multi-evidence question
answering task, the CFC obtains a new state-of-the-art result of 70.6% on the
blind test set, outperforming the previous best by 3% accuracy despite not
using pretrained contextual encoders.
