Journal of Computer Science and Technology ›› 2022, Vol. 37 ›› Issue (3): 527-538.doi: 10.1007/s11390-022-2029-5

Special Issue: Artificial Intelligence and Pattern Recognition

• Special Section on Self-Learning with Deep Neural Networks • Previous Articles     Next Articles

Self-Supervised Task Augmentation for Few-Shot Intent Detection

Peng-Fei Sun (孙鹏飞), Ya-Wen Ouyang (欧阳亚文), Ding-Jie Song (宋定杰), and Xin-Yu Dai* (戴新宇), Member, CCF        

  1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
  • Received:2021-11-19 Revised:2022-03-10 Accepted:2022-04-08 Online:2022-05-30 Published:2022-05-30
  • Contact: Xin-Yu Dai E-mail:daixinyu@nju.edu.cn
  • About author:Xin-Yu Dai received his B.E. and Ph.D. degrees in computer science from Nanjing University, Nanjing, in 1999 and 2005, respectively. He has been on leave from Nanjing University, Nanjing, from August 2010 to September 2011 to visit EECS Department and Statistics Department at UC Berkeley, Berkeley. He is currently a professor with the National Key Laboratory for Novel Software Technology and Department of Computer Science and Technology at Nanjing University, Nanjing. His research interests include natural language processing and knowledge engineering.
  • Supported by:
    This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61936012 and 61976114.

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Few-shot intent detection is a practical challenge task, because new intents are frequently emerging and collecting large-scale data for them could be costly. Meta-learning, a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks, has been a popular way to tackle this problem. However, the existing meta-learning models have been evidenced to be overfitting when the meta-training tasks are insufficient. To overcome this challenge, we present a novel self-supervised task augmentation with meta-learning framework, namely STAM. Firstly, we introduce the task augmentation, which explores two different strategies and combines them to extend meta-training tasks. Secondly, we devise two auxiliary losses for integrating self-supervised learning into meta-learning to learn more generalizable and transferable features. Experimental results show that STAM can achieve consistent and considerable performance improvement to existing state-of-the-art methods on four datasets.

Key words: self-supervised learning; task augmentation; meta-learning; few-shot intent detection;

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