  Representation learning is the foundation of machine reading comprehension
and inference. In state-of-the-art models, character-level representations have
been broadly adopted to alleviate the problem of effectively representing rare
or complex words. However, character itself is not a natural minimal linguistic
unit for representation or word embedding composing due to ignoring the
linguistic coherence of consecutive characters inside word. This paper presents
a general subword-augmented embedding framework for learning and composing
computationally-derived subword-level representations. We survey a series of
unsupervised segmentation methods for subword acquisition and different
subword-augmented strategies for text understanding, showing that
subword-augmented embedding significantly improves our baselines in various
types of text understanding tasks on both English and Chinese benchmarks.
