Effective Character-Augmented Word Embedding for Machine Reading Comprehension

By Zhuosheng Zhang, Yafang Huang, Peng Fei Zhu, Hai Zhao. 2018

17 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Dual Co-Matching Network for Multi-choice Reading Comprehension (2019, background): “Machine reading comprehension (MRC) is a fundamental and long-standing goal of natural language understanding which aims to teach machine to answer question automatically according to given passage (Hermann et al. 2015; Ra-jpurkar et al. 2016; Nguyen et al. 2016; Zhang et al. 2018).”
Restoring ancient text using deep learning: a case study on Greek epigraphy (2019, background): “ shares similarities with machine reading comprehension (Hermann et al.,2015;Kociskˇ y et al.` ,2018), and cloze deletion tests (Hill et al.,2016;Bajgar et al.,2017; Fedus et al.,2018;Xie et al.,2018;Zhang et al., 2018). Although word-level language modelling is capable of capturing context informat”