  Machine reading comprehension is a task to model relationship between passage
and query. In terms of deep learning framework, most of state-of-the-art models
simply concatenate word and character level representations, which has been
shown suboptimal for the concerned task. In this paper, we empirically explore
different integration strategies of word and character embeddings and propose a
character-augmented reader which attends character-level representation to
augment word embedding with a short list to improve word representations,
especially for rare words. Experimental results show that the proposed approach
helps the baseline model significantly outperform state-of-the-art baselines on
various public benchmarks.
