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.

通过利用子词增强嵌入式框架，本文介绍了一种学习和合成计算产生的子词级别表示的新方法，经过在英文和汉语测试基准上的各种文本理解任务中的实验测试，发现子词增强嵌入式显着改善了我们在各种文本理解任务上的基线。