Remarkable success has been achieved in the last few years on some limited
machine reading comprehension (MRC) tasks. However, it is still difficult to
interpret the predictions of existing MRC models. In this paper, we focus on
extracting evidence sentences that can explain or support the answers of
multiple-choice MRC tasks, where the majority of answer options cannot be
directly extracted from reference documents.
Due to the lack of ground truth evidence sentence labels in most cases, we
apply distant supervision to generate imperfect labels and then use them to
train an evidence sentence extractor. To denoise the noisy labels, we apply a
recently proposed deep probabilistic logic learning framework to incorporate
both sentence-level and cross-sentence linguistic indicators for indirect
supervision. We feed the extracted evidence sentences into existing MRC models
and evaluate the end-to-end performance on three challenging multiple-choice
MRC datasets: MultiRC, RACE, and DREAM, achieving comparable or better
performance than the same models that take as input the full reference
document. To the best of our knowledge, this is the first work extracting
evidence sentences for multiple-choice MRC.

本研究提出一种利用深度概率逻辑学习框架提取证据句子的方法，在三个具有挑战性的多项选择机器阅读理解数据集上实现了可比较或更好的性能，为多项选择机器阅读理解提供了新的思路。