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arXiv · 1811.02364

Effective Subword Segmentation for Text Comprehension

Zhuosheng Zhang·Hai Zhao·Kangwei Ling·Jiangtong Li·Zuchao Li·Shexia He·Guohong Fu
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Abstract

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.

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Zhuosheng Zhang, Hai Zhao, Kangwei Ling, Jiangtong Li, Zuchao Li, Shexia He, Guohong Fu. 2019-06-11. Effective Subword Segmentation for Text Comprehension. https://arxiv.org/abs/1811.02364

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