  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.
