  Spoken Language Understanding (SLU) is composed of two subtasks: intent
detection (ID) and slot filling (SF). There are two lines of research on SLU.
One jointly tackles these two subtasks to improve their prediction accuracy,
and the other focuses on the domain-adaptation ability of one of the subtasks.
In this paper, we attempt to bridge these two lines of research and propose a
joint and domain adaptive approach to SLU. We formulate SLU as a constrained
generation task and utilize a dynamic vocabulary based on domain-specific
ontology. We conduct experiments on the ASMixed and MTOD datasets and achieve
competitive performance with previous state-of-the-art joint models. Besides,
results show that our joint model can be effectively adapted to a new domain.
