  Meta-learning is widely used for few-shot slot tagging in task of few-shot
learning. The performance of existing methods is, however, seriously affected
by \textit{sample forgetting issue}, where the model forgets the historically
learned meta-training tasks while solely relying on support sets when adapting
to new tasks. To overcome this predicament, we propose the
\textbf{M}emory-based \textbf{C}ontrastive \textbf{M}eta-\textbf{L}earning
(aka, MCML) method, including \textit{learn-from-the-memory} and
\textit{adaption-from-the-memory} modules, which bridge the distribution gap
between training episodes and between training and testing respectively.
Specifically, the former uses an explicit memory bank to keep track of the
label representations of previously trained episodes, with a contrastive
constraint between the label representations in the current episode with the
historical ones stored in the memory. In addition, the
\emph{adaption-from-memory} mechanism is introduced to learn more accurate and
robust representations based on the shift between the same labels embedded in
the testing episodes and memory. Experimental results show that the MCML
outperforms several state-of-the-art methods on both SNIPS and NER datasets and
demonstrates strong scalability with consistent improvement when the number of
shots gets greater.
