  In this paper, we investigate few-shot joint learning for dialogue language
understanding. Most existing few-shot models learn a single task each time with
only a few examples. However, dialogue language understanding contains two
closely related tasks, i.e., intent detection and slot filling, and often
benefits from jointly learning the two tasks. This calls for new few-shot
learning techniques that are able to capture task relations from only a few
examples and jointly learn multiple tasks. To achieve this, we propose a
similarity-based few-shot learning scheme, named Contrastive Prototype Merging
network (ConProm), that learns to bridge metric spaces of intent and slot on
data-rich domains, and then adapt the bridged metric space to the specific
few-shot domain. Experiments on two public datasets, Snips and FewJoint, show
that our model significantly outperforms the strong baselines in one and five
shots settings.
