  Natural language understanding includes the tasks of intent detection
(identifying a user's objectives) and slot filling (extracting the entities
relevant to those objectives). Prior slot filling methods assume that each
intent type cannot occur more than once within a message, however this is often
not a valid assumption for real-world settings. In this work, we generalize
slot filling by removing the constraint of unique intents in a message. We cast
this as a JSON generation task and approach it using a language model. We
create a pre-training dataset by combining DBpedia and existing slot filling
datasets that we convert for JSON generation. We also generate an in-domain
dataset using GPT-3. We train T5 models for this task (with and without
exemplars in the prompt) and find that both training datasets improve
performance, and that the model is able to generalize to intent types not seen
during training.
