  Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct
answer from a set of options based on a given passage and question. Due to task
specific of MMRC, it is non-trivial to transfer knowledge from other MRC tasks
such as SQuAD, Dream. In this paper, we simply 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. We construct our model upon ALBERT-xxlarge model and estimate it on the
RACE dataset. During training, We adopt AutoML strategy to tune better
parameters. Experimental results show that the single-choice is better than
multi-choice. In addition, by transferring knowledge from other kinds of MRC
tasks, our model achieves a new state-of-the-art results in both single and
ensemble settings.
