In this paper, we present a novel approach to machine reading comprehension
for the MS-MARCO dataset. Unlike the SQuAD dataset that aims to answer a
question with exact text spans in a passage, the MS-MARCO dataset defines the
task as answering a question from multiple passages and the words in the answer
are not necessary in the passages. We therefore develop an
extraction-then-synthesis framework to synthesize answers from extraction
results. Specifically, the answer extraction model is first employed to predict
the most important sub-spans from the passage as evidence, and the answer
synthesis model takes the evidence as additional features along with the
question and passage to further elaborate the final answers. We build the
answer extraction model with state-of-the-art neural networks for single
passage reading comprehension, and propose an additional task of passage
ranking to help answer extraction in multiple passages. The answer synthesis
model is based on the sequence-to-sequence neural networks with extracted
evidences as features. Experiments show that our extraction-then-synthesis
method outperforms state-of-the-art methods.

本文提出了一种新的机器阅读理解方法，使用抽取 - 综合框架来解决 MS-MARCO 数据集的问题，该方法使用先进的神经网络技术来构建具有单篇阅读理解的答案抽取模型，并使用序列到序列神经网络作为答案综合模型，实现了对多篇文献的问答。实验结果表明，这种方法的性能超过了现有的最佳方法。