Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 188-194.DOI: 10.3778/j.issn.1002-8331.2102-0235

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Intent Detection of Domain Adaptation Combined with Capsule Network

ZHAO Pengfei, LI Yanling, LIN Min   

  1. College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China
  • Online:2021-11-01 Published:2021-11-04



  1. 内蒙古师范大学 计算机科学技术学院,呼和浩特 010022


Intent detection is an important task in spoken language understanding, which is related to the performance of the entire dialogue system. Aiming at the problem of less training corpus in the human-machine dialogue system in the new domain, and the construction of training corpus is very expensive. This thesis proposes a domain adaptation method using capsule network to improve the domain discriminator. This method uses a domain adversarial neural network to transfer the feature information of the source domain to the target domain, in addition, in order to ensure the feature quality of the domain intent text, the feature representations of the source domain and the target domain are extracted again, which can fully obtain the feature information of the intent text, captures the unique features of different domains, improves the discriminator ability of the domain, and ensures the reliability of the guarantee domain adaptation tasks. When the target domain contains only a small number of labeled samples, the accuracy rate on Chinese and English datasets reaches 83.3% and 88.9%.

Key words: intent detection, dialogue system, capsule network, domain adaptation



关键词: 意图识别, 对话系统, 胶囊网络, 领域适应