Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies
  and Simple Labels
Danilo Ribeiro Omid Abdar Jack Goetz Mike Ross Annie Dong Kenneth Forbus Ahmed Mohamed
Frame semantic parsing is an important component of task-oriented dialogue
systems. Current models rely on a significant amount training data to
successfully identify the intent and slots in the user's input utterance. This
creates a significant barrier for adding new domains to virtual assistant
capabilities, as creation of this data requires highly specialized NLP
expertise. In this work we propose OpenFSP, a framework that allows for easy
creation of new domains from a handful of simple labels that can be generated
without specific NLP knowledge. Our approach relies on creating a small, but
expressive, set of domain agnostic slot types that enables easy annotation of
new domains. Given such annotation, a matching algorithm relying on sentence
encoders predicts the intent and slots for domains defined by end-users.
Extensive experiments on the TopV2 dataset shows that our model outperforms
strong baselines in this simple labels setting.