CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Employing External Rich Knowledge for Machine Comprehension
Wang Bingning; Guo Shangmin; Liu Kang; He Shizhu; Zhao Jun
2016
Conference NameProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Pages2929-2935
Conference Date2016-7
Conference Place美国纽约
AbstractRecently proposed machine comprehension (MC) applicationisanefforttodealwithnaturallanguage understanding problem. However, the small size of machine comprehension labeled data confines the application of deep neural networks architectures that have shown advantage in semantic inference tasks. Previous methods use a lot of NLP tools to extract linguistic features but only gain little improvement over simple baseline. In this paper, we build an attention-based recurrent neural network model, train it with the help of external knowledge which is semantically relevant to machine comprehension, and achieves a new state-of-the-art result.
KeywordMachine Comprehension Question Answering Deep Learning
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20206
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorLiu Kang
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Wang Bingning,Guo Shangmin,Liu Kang,et al. Employing External Rich Knowledge for Machine Comprehension[C],2016:2929-2935.
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