CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
SHTM: A Neocortex-inspired Algorithm for One-shot Text Generation
Wang YW(王寓巍)1,2; Ceng Y(曾毅)1,2; Xu B(徐波)1,2
2016-10
Conference Name2016 IEEE International Conference on Systems, Man, and Cybernetics
Conference Date2016-10-09--2016-10-12
Conference PlaceBudapest, Hungary 978-1-
Abstract
Text generation is a typical nature language processing
task, and is the basis of machine translation and question
answering. Deep learning techniques can get good performance
on this task under the condition that huge number of parameters
and mass of data are available for training. However, human
beings do not learn in this way. People combine knowledge
learned before and something new with only few samples. This
process is called one-shot learning. In this paper, we propose
a neocortex based computational model, Semantic Hierarchical
Temporal Memory model (SHTM), for one-shot text generation.
The model is refined from Hierarchical Temporal Memory model.
LSTM is used for comparative study. Results on three public
datasets show that SHTM performs much better than LSTM on
the measures of mean precision and BLEU score. In addition,
we utilize SHTM model to do question answering in the fashion
of text generation and verifying its superiority.
KeywordOne-shot Learing Htm Text Generation
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14744
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
Recommended Citation
GB/T 7714
Wang YW,Ceng Y,Xu B. SHTM: A Neocortex-inspired Algorithm for One-shot Text Generation[C],2016.
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