CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Learning to Activate Logic Rules for Textual Reasoning
Yao, Yiqun; Xu, Jiaming; Shi, Jing; Xu, Bo
Source PublicationNEURAL NETWORKS
2018
Issue106Pages:42-49
AbstractMost current textual reasoning models cannot learn human-like reasoning process, and thus lack interpretability and logical accuracy. To help address this issue, we propose a novel reasoning model which learns to activate logic rules explicitly via deep reinforcement learning. It takes the form of Memory Networks but features a special memory that stores relational tuples, mimicking the ‘‘Image Schema’’ in human cognitive activities. We redefine textual reasoning as a sequential decision-making process modifying or retrieving from the memory, where logic rules serve as state-transition functions. Activating logic rules for reasoning involves two problems: variable binding and relation activating, and this is a first step to solve them jointly. Our model achieves an average error rate of 0.7% on bAbI-20, a widely-used synthetic reasoning benchmark, using less than 1k training samples and no supporting facts.
KeywordNatural Language Reasoning Memory Networks Image Schema Logic Rules Reinforcement Learning
Indexed BySCI
Language英语
WOS IDWOS:000445015200005
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21756
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Recommended Citation
GB/T 7714
Yao, Yiqun,Xu, Jiaming,Shi, Jing,et al. Learning to Activate Logic Rules for Textual Reasoning[J]. NEURAL NETWORKS,2018(106):42-49.
APA Yao, Yiqun,Xu, Jiaming,Shi, Jing,&Xu, Bo.(2018).Learning to Activate Logic Rules for Textual Reasoning.NEURAL NETWORKS(106),42-49.
MLA Yao, Yiqun,et al."Learning to Activate Logic Rules for Textual Reasoning".NEURAL NETWORKS .106(2018):42-49.
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