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.

提出了一种基于端到端神经模型的多文本阅读理解模式，通过多个模块的共同作用来检测文本间的答案验证，实现对真实网络数据的阅读理解，显著提高了 MS-MARCO 数据集和中文 DuReader 数据集的 MRC 模型性能。

跨段落答案验证的多段机器阅读理解

Multi-Passage Machine Reading Comprehension with Cross-Passage Answer  Verification

Question Answering has come a long way from answer sentence selection,
relational QA to reading and comprehension. We shift our attention to
generative question answering (gQA) by which we facilitate machine to read
passages and answer questions by learning to generate the answers. We frame the
problem as a generative task where the encoder being a network that models the
relationship between question and passage and encoding them to a vector thus
facilitating the decoder to directly form an abstraction of the answer. Not
being able to retain facts and making repetitions are common mistakes that
affect the overall legibility of answers. To counter these issues, we employ
copying mechanism and maintenance of coverage vector in our model respectively.
Our results on MS-MARCO demonstrate it's superiority over baselines and we also
show qualitative examples where we improved in terms of correctness and
readability

本研究通过使用编码 - 解码模型，结合复制机制和覆盖向量维护，实现了生成式问答，并在 MS-MARCO 数据集上验证了该方法的出色表现。