  Most existing multi-document machine reading comprehension models mainly
focus on understanding the interactions between the input question and
documents, but ignore following two kinds of understandings. First, to
understand the semantic meaning of words in the input question and documents
from the perspective of each other. Second, to understand the supporting cues
for a correct answer from the perspective of intra-document and
inter-documents. Ignoring these two kinds of important understandings would
make the models oversee some important information that may be helpful for
inding correct answers. To overcome this deiciency, we propose a deep
understanding based model for multi-document machine reading comprehension. It
has three cascaded deep understanding modules which are designed to understand
the accurate semantic meaning of words, the interactions between the input
question and documents, and the supporting cues for the correct answer. We
evaluate our model on two large scale benchmark datasets, namely TriviaQA Web
and DuReader. Extensive experiments show that our model achieves
state-of-the-art results on both datasets.
