Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct
answer from a set of options based on a given passage and question. The
existing methods employ the pre-trained language model as the encoder, share
and transfer knowledge through fine-tuning.These methods mainly focus on the
design of exquisite mechanisms to effectively capture the relationships among
the triplet of passage, question and answers. It is non-trivial but ignored to
transfer knowledge from other MRC tasks such as SQuAD due to task specific of
MMRC.In this paper, we reconstruct multi-choice to single-choice by training a
binary classification to distinguish whether a certain answer is correct. Then
select the option with the highest confidence score as the final answer. Our
proposed method gets rid of the multi-choice framework and can leverage
resources of other tasks. We construct our model based on the ALBERT-xxlarge
model and evaluate it on the RACE and DREAM datasets. Experimental results show
that our model performs better than multi-choice methods. In addition, by
transferring knowledge from other kinds of MRC tasks, our model achieves
state-of-the-art results in both single and ensemble settings.

该研究论文以多选机器阅读理解为主题，使用预训练语言模型作为编码器，并通过微调来分享和传递知识。通过训练二元分类，将多选问题转换为单选问题，并选择置信度最高的选项作为最终答案。通过从其他 MRC 任务转移知识，提出的方法摆脱了多选框架，并可以利用其他任务的资源。在 RACE 和 DREAM 数据集上评估基于 ALBERT-xxlarge 模型构建的模型，实验结果表明该模型在多选方法上表现更好，并且通过从其他类型 MRC 任务转移知识，该模型在单个和集合设置中都取得了最先进的结果。