Understand
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.
Reading the bibliography…