Data Synthesis for Domain Development of Natural Language Understanding for Assistant Systems

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

• receive a request to train a natural-language understanding (NLU) model for a new domain;
• access a context-free grammar associated with the new domain, wherein the context-free grammar defines one or more production rules with respect to ontology tokens associated with the new domain and utterance tokens for generating natural-language strings in the new domain;
• generate a plurality of utterance-frame pairs based on traversing a hierarchical grammar tree associated with the context-free grammar based on the one or more production rules, wherein each utterance-frame pair comprises an utterance and a corresponding frame, wherein each frame comprises one or more ontology tokens associated with the new domain and one or more utterance tokens corresponding to one or more of the ontology tokens of the frame;
• and
• train the NLU model based on the plurality of utterance-frame pairs.

A system comprising:

• one or more processors;
• and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:
• receive a request to train a natural-language understanding (NLU) model for a new domain;
• access a context-free grammar associated with the new domain, wherein the context-free grammar defines one or more production rules with respect to ontology tokens associated with the new domain and utterance tokens for generating natural-language strings in the new domain;
• generate a plurality of utterance-frame pairs based on traversing a hierarchical grammar tree associated with the context-free grammar based on the one or more production rules, wherein each utterance-frame pair comprises an utterance and a corresponding frame, wherein each frame comprises one or more ontology tokens associated with the new domain and one or more utterance tokens corresponding to one or more of the ontology tokens of the frame;
• and
• train the NLU model based on the plurality of utterance-frame pairs.