  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.
