  Multi-choice Machine Reading Comprehension (MRC) is a major and challenging
task for machines to answer questions according to provided options. Answers in
multi-choice MRC cannot be directly extracted in the given passages, and
essentially require machines capable of reasoning from accurate extracted
evidence. However, the critical evidence may be as simple as just one word or
phrase, while it is hidden in the given redundant, noisy passage with multiple
linguistic hierarchies from phrase, fragment, sentence until the entire
passage. We thus propose a novel general-purpose model enhancement which
integrates multi-grained evidence comprehensively, named Multi-grained evidence
inferencer (Mugen), to make up for the inability. Mugen extracts three
different granularities of evidence: coarse-, middle- and fine-grained
evidence, and integrates evidence with the original passages, achieving
significant and consistent performance improvement on four multi-choice MRC
benchmarks.
