  Machine reading comprehension (MRC) on real web data usually requires the
machine to answer a question by analyzing multiple passages retrieved by search
engine. Compared with MRC on a single passage, multi-passage MRC is more
challenging, since we are likely to get multiple confusing answer candidates
from different passages. To address this problem, we propose an end-to-end
neural model that enables those answer candidates from different passages to
verify each other based on their content representations. Specifically, we
jointly train three modules that can predict the final answer based on three
factors: the answer boundary, the answer content and the cross-passage answer
verification. The experimental results show that our method outperforms the
baseline by a large margin and achieves the state-of-the-art performance on the
English MS-MARCO dataset and the Chinese DuReader dataset, both of which are
designed for MRC in real-world settings.
