Effective Subword Segmentation for Text Comprehension
By Zhuosheng Zhang, Zhao Hai, Kangwei Ling, Jiangtong Li, Zuchao Li, Shexia He, Guohong Fu. 2018
30 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Natural Language Processing
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Citations in the corpus
- Dual Co-Matching Network for Multi-choice Reading Comprehension (2019, methodology): “…is essential and indeed extremely helpful (Devlin et al. 2019; Yang et al. 2019) but at the same time it is time-consuming and resource-demanding to impart massive amounts of general knowledge from external corpora into a deep language model via pre-training (Sun et al. 2019; Zhang et al. 2019b).”