CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Memory Augmented Attention Model for Chinese Implicit Discourse Relation Recognition
Yang Liu; Jiajun Zhang; Chengqing Zong
2017
Conference Name全国第十六届计算语言学会议(The Sixteenth China National Conference on Computational Linguistics, CCL 2017)
Conference Date2017-10
Conference Place南京
Abstract

Recently, Chinese implicit discourse relation recognition has attracted
more and more attention, since it is crucial to understand the Chinese discourse text. In this paper, we propose a novel memory augmented attention model which represents the arguments using an attention-based neural network and preserves the crucial information with an external memory network which captures each discourse relation clustering structure to support the relation inference. Extensive experiments demonstrate that our proposed model can achieve the new state-of-the-art results on Chinese Discourse Treebank. We further leverage network
visualization to show why our attention and memory model are effective.
 

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23998
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorYang Liu
AffiliationInstitute of automation, Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yang Liu,Jiajun Zhang,Chengqing Zong. Memory Augmented Attention Model for Chinese Implicit Discourse Relation Recognition[C],2017.
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