@inproceedings{neves-ribeiro-etal-2023-towards,
title = "Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels",
author = "Neves Ribeiro, Danilo and
Goetz, Jack and
Abdar, Omid and
Ross, Mike and
Dong, Annie and
Forbus, Kenneth and
Mohamed, Ahmed",
editor = "Surdeanu, Mihai and
Riloff, Ellen and
Chiticariu, Laura and
Frietag, Dayne and
Hahn-Powell, Gus and
Morrison, Clayton T. and
Noriega-Atala, Enrique and
Sharp, Rebecca and
Valenzuela-Escarcega, Marco",
booktitle = "Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.pandl-1.6",
doi = "10.18653/v1/2023.pandl-1.6",
pages = "54--63",
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. Experiments on the TopV2 dataset shows that our model trained on these simple labels have strong performance against supervised baselines.",
}
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<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. Experiments on the TopV2 dataset shows that our model trained on these simple labels have strong performance against supervised baselines.</abstract>
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%0 Conference Proceedings
%T Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels
%A Neves Ribeiro, Danilo
%A Goetz, Jack
%A Abdar, Omid
%A Ross, Mike
%A Dong, Annie
%A Forbus, Kenneth
%A Mohamed, Ahmed
%Y Surdeanu, Mihai
%Y Riloff, Ellen
%Y Chiticariu, Laura
%Y Frietag, Dayne
%Y Hahn-Powell, Gus
%Y Morrison, Clayton T.
%Y Noriega-Atala, Enrique
%Y Sharp, Rebecca
%Y Valenzuela-Escarcega, Marco
%S Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F neves-ribeiro-etal-2023-towards
%X 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. Experiments on the TopV2 dataset shows that our model trained on these simple labels have strong performance against supervised baselines.
%R 10.18653/v1/2023.pandl-1.6
%U https://aclanthology.org/2023.pandl-1.6
%U https://doi.org/10.18653/v1/2023.pandl-1.6
%P 54-63
Markdown (Informal)
[Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels](https://aclanthology.org/2023.pandl-1.6) (Neves Ribeiro et al., PANDL-WS 2023)
ACL