  Multiple-choice reading comprehension (MCRC) is the task of selecting the
correct answer from multiple options given a question and an article. Existing
MCRC models typically either read each option independently or compute a
fixed-length representation for each option before comparing them. However,
humans typically compare the options at multiple-granularity level before
reading the article in detail to make reasoning more efficient. Mimicking
humans, we propose an option comparison network (OCN) for MCRC which compares
options at word-level to better identify their correlations to help reasoning.
Specially, each option is encoded into a vector sequence using a skimmer to
retain fine-grained information as much as possible. An attention mechanism is
leveraged to compare these sequences vector-by-vector to identify more subtle
correlations between options, which is potentially valuable for reasoning.
Experimental results on the human English exam MCRC dataset RACE show that our
model outperforms existing methods significantly. Moreover, it is also the
first model that surpasses Amazon Mechanical Turker performance on the whole
dataset.
