  We propose a generative framework for joint sequence labeling and
sentence-level classification. Our model performs multiple sequence labeling
tasks at once using a single, shared natural language output space. Unlike
prior discriminative methods, our model naturally incorporates label semantics
and shares knowledge across tasks. Our framework is general purpose, performing
well on few-shot, low-resource, and high-resource tasks. We demonstrate these
advantages on popular named entity recognition, slot labeling, and intent
classification benchmarks. We set a new state-of-the-art for few-shot slot
labeling, improving substantially upon the previous 5-shot ($75.0\% \rightarrow
90.9\%$) and 1-shot ($70.4\% \rightarrow 81.0\%$) state-of-the-art results.
Furthermore, our model generates large improvements ($46.27\% \rightarrow
63.83\%$) in low-resource slot labeling over a BERT baseline by incorporating
label semantics. We also maintain competitive results on high-resource tasks,
performing within two points of the state-of-the-art on all tasks and setting a
new state-of-the-art on the SNIPS dataset.
