  Representation learning is the foundation of machine reading comprehension.
In state-of-the-art models, deep learning methods broadly use word and
character level representations. However, character is not naturally the
minimal linguistic unit. In addition, with a simple concatenation of character
and word embedding, previous models actually give suboptimal solution. In this
paper, we propose to use subword rather than character for word embedding
enhancement. We also empirically explore different augmentation strategies on
subword-augmented embedding to enhance the cloze-style reading comprehension
model reader. In detail, we present a reader that uses subword-level
representation to augment word embedding with a short list to handle rare words
effectively. A thorough examination is conducted to evaluate the comprehensive
performance and generalization ability of the proposed reader. Experimental
results show that the proposed approach helps the reader significantly
outperform the state-of-the-art baselines on various public datasets.
