  While most successful approaches for machine reading comprehension rely on
single training objective, it is assumed that the encoder layer can learn great
representation through the loss function we define in the predict layer, which
is cross entropy in most of time, in the case that we first use neural networks
to encode the question and paragraph, then directly fuse the encoding result of
them. However, due to the distantly loss backpropagating in reading
comprehension, the encoder layer cannot learn effectively and be directly
supervised. Thus, the encoder layer can not learn the representation well at
any time. Base on this, we propose to inject multi granularity information to
the encoding layer. Experiments demonstrate the effect of adding multi
granularity information to the encoding layer can boost the performance of
machine reading comprehension system. Finally, empirical study shows that our
approach can be applied to many existing MRC models.
