  Intent classification (IC) and slot filling (SF) are critical building blocks
in task-oriented dialogue systems. These two tasks are closely-related and can
flourish each other. Since only a few utterances can be utilized for
identifying fast-emerging new intents and slots, data scarcity issue often
occurs when implementing IC and SF. However, few IC/SF models perform well when
the number of training samples per class is quite small. In this paper, we
propose a novel explicit-joint and supervised-contrastive learning framework
for few-shot intent classification and slot filling. Its highlights are as
follows. (i) The model extracts intent and slot representations via
bidirectional interactions, and extends prototypical network to achieve
explicit-joint learning, which guarantees that IC and SF tasks can mutually
reinforce each other. (ii) The model integrates with supervised contrastive
learning, which ensures that samples from same class are pulled together and
samples from different classes are pushed apart. In addition, the model follows
a not common but practical way to construct the episode, which gets rid of the
traditional setting with fixed way and shot, and allows for unbalanced
datasets. Extensive experiments on three public datasets show that our model
can achieve promising performance.
