  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.
