Machine Reading Comprehension (MRC) for question answering (QA), which aims
to answer a question given the relevant context passages, is an important way
to test the ability of intelligence systems to understand human language.
Multiple-Choice QA (MCQA) is one of the most difficult tasks in MRC because it
often requires more advanced reading comprehension skills such as logical
reasoning, summarization, and arithmetic operations, compared to the extractive
counterpart where answers are usually spans of text within given passages.
Moreover, most existing MCQA datasets are small in size, making the learning
task even harder. We introduce MMM, a Multi-stage Multi-task learning framework
for Multi-choice reading comprehension. Our method involves two sequential
stages: coarse-tuning stage using out-of-domain datasets and multi-task
learning stage using a larger in-domain dataset to help model generalize better
with limited data. Furthermore, we propose a novel multi-step attention network
(MAN) as the top-level classifier for this task. We demonstrate MMM
significantly advances the state-of-the-art on four representative MCQA
datasets.

本研究提出了一种名为 MMM 的基于多阶段多任务学习框架的多选阅读理解方法，其中采用了一种名为多步注意力网络 (MAN) 的新型分类器。实验证明该方法在四个代表性 MCQA 数据集上显著提高了最新技术水平。