2018/08/07 by Zhuosheng Zhang, Zhang, Zhuosheng, Yafang Huang +5
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Text Readability and Simplification #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.1808.02772
Accepted by NLPCC 2018. Early work of arXiv:1806.09103
openalex publication_date 2018/08/07 · openalex created_date 2019/06/27 · arxiv created 2021/01/07 · arxiv updated 2021/01/08 · openalex updated_date 2026/07/28
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.