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

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 assista...

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Bibliographic Details
Published in:arXiv.org 2023-05
Main Authors: Ribeiro, Danilo, Abdar, Omid, Goetz, Jack, Ross, Mike, Dong, Annie, bus, Kenneth, Ahmed, Mohamed
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Artificial intelligence
Coders
Domains
Labels
Semantics
Online Access:Get full text
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Summary: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.
ISSN:2331-8422