Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels

• URL: http://arxiv.org/abs/2305.03793v1
• Date: Fri, 5 May 2023 18:47:18 GMT
• Title: Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels
• Authors: Danilo Ribeiro, Omid Abdar, Jack Goetz, Mike Ross, Annie Dong, Kenneth Forbus, Ahmed Mohamed
• Abstract summary: OpenFSP is a framework for easy creation of new domains from simple labels. Our approach relies on creating a small, but expressive, set of domain agnostic slot types. Our model outperforms strong baselines in this simple labels setting.
• Score: 0.9236074230806577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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