MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot
  Slot Tagging
  Meta-learning is widely used for few-shot slot tagging in the task of
few-shot learning. The performance of existing methods is, however, seriously
affected by catastrophic forgetting. This phenomenon is common in deep learning
as the training and testing modules fail to take into account historical
information, i.e. previously trained episodes in the metric-based
meta-learning. To overcome this predicament, we propose the Memory-based
Contrastive Meta-learning (MCML) method. Specifically, we propose a
learn-from-memory mechanism that use explicit memory to keep track of the label
representations of previously trained episodes and propose a contrastive
learning method to compare the current label embedded in the few shot episode
with the historic ones stored in the memory, and an adaption-from memory
mechanism to determine the output label based on the contrast between the input
labels embedded in the test episode and the label clusters in the memory.
Experimental results show that MCML is scalable and outperforms metric-based
meta-learning and optimization-based meta-learning on all 1shot, 5-shot,
10-shot, and 20-shot scenarios of the SNIPS dataset.
