CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Inner Attention based Recurrent Neural Networks for Answer Selection
Wang Bingning; Liu Kang; Zhao Jun
2016
Conference NameProceedings of the 54th Annual Meeting of the Association for Computational Linguistics 
VolumeVolumn 1, Long Paper
Pages1288-1297
Conference Date2016
Conference Place德国
Abstract
Attention based recurrent neural networks have shown advantages in representing natural language sentences (Hermann et al., 2015; Rocktäschel et al., 2015; Tan et al., 2015). Based on recurrent neural networks (RNN), external attention information was added to hidden representations to get an attentive sentence representation. Despite the improvement over non- attentive models, the attention mechanism under RNN is not well studied. In this work, we analyze the deficiency of traditional attention based RNN models quantitatively and qualitatively. Then we present three new RNN models that add attention information before RNN hidden representation, which shows advantage in representing sentence and achieves new state- of-art results in answer selection task.
KeywordAnswer Selection Question Answering Deep Learning
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20182
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,Liu Kang,Zhao Jun. Inner Attention based Recurrent Neural Networks for Answer Selection[C],2016:1288-1297.
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