  Task-oriented semantic parsing models typically have high resource
requirements: to support new ontologies (i.e., intents and slots),
practitioners crowdsource thousands of samples for supervised fine-tuning.
Partly, this is due to the structure of de facto copy-generate parsers; these
models treat ontology labels as discrete entities, relying on parallel data to
extrinsically derive their meaning. In our work, we instead exploit what we
intrinsically know about ontology labels; for example, the fact that
SL:TIME_ZONE has the categorical type "slot" and language-based span "time
zone". Using this motivation, we build our approach with offline and online
stages. During preprocessing, for each ontology label, we extract its intrinsic
properties into a component, and insert each component into an inventory as a
cache of sorts. During training, we fine-tune a seq2seq, pre-trained
transformer to map utterances and inventories to frames, parse trees comprised
of utterance and ontology tokens. Our formulation encourages the model to
consider ontology labels as a union of its intrinsic properties, therefore
substantially bootstrapping learning in low-resource settings. Experiments show
our model is highly sample efficient: using a low-resource benchmark derived
from TOPv2, our inventory parser outperforms a copy-generate parser by +15 EM
absolute (44% relative) when fine-tuning on 10 samples from an unseen domain.
