@inproceedings{yu-etal-2022-knowledge,
title = "Knowledge-grounded Dialog State Tracking",
author = "Yu, Dian and
Wang, Mingqiu and
Cao, Yuan and
El Shafey, Laurent and
Shafran, Izhak and
Soltau, Hagen",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.250",
doi = "10.18653/v1/2022.findings-emnlp.250",
pages = "3428--3435",
abstract = "Knowledge (including structured knowledge such as schema and ontology and unstructured knowledge such as web corpus) is a critical part of dialog understanding, especially for unseen tasks and domains. Traditionally, such domain-specific knowledge is encoded implicitly into model parameters for the execution of downstream tasks, which makes training inefficient. In addition , such models are not easily transferable to new tasks with different schemas. In this work, we propose to perform dialog state tracking grounded on knowledge encoded externally. We query relevant knowledge of various forms based on the dialog context where such information can grounds the prediction of dialog states. We demonstrate superior performance of our proposed method over strong baselines, especially in the few-shot learning setting.",
}
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<abstract>Knowledge (including structured knowledge such as schema and ontology and unstructured knowledge such as web corpus) is a critical part of dialog understanding, especially for unseen tasks and domains. Traditionally, such domain-specific knowledge is encoded implicitly into model parameters for the execution of downstream tasks, which makes training inefficient. In addition , such models are not easily transferable to new tasks with different schemas. In this work, we propose to perform dialog state tracking grounded on knowledge encoded externally. We query relevant knowledge of various forms based on the dialog context where such information can grounds the prediction of dialog states. We demonstrate superior performance of our proposed method over strong baselines, especially in the few-shot learning setting.</abstract>
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%0 Conference Proceedings
%T Knowledge-grounded Dialog State Tracking
%A Yu, Dian
%A Wang, Mingqiu
%A Cao, Yuan
%A El Shafey, Laurent
%A Shafran, Izhak
%A Soltau, Hagen
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F yu-etal-2022-knowledge
%X Knowledge (including structured knowledge such as schema and ontology and unstructured knowledge such as web corpus) is a critical part of dialog understanding, especially for unseen tasks and domains. Traditionally, such domain-specific knowledge is encoded implicitly into model parameters for the execution of downstream tasks, which makes training inefficient. In addition , such models are not easily transferable to new tasks with different schemas. In this work, we propose to perform dialog state tracking grounded on knowledge encoded externally. We query relevant knowledge of various forms based on the dialog context where such information can grounds the prediction of dialog states. We demonstrate superior performance of our proposed method over strong baselines, especially in the few-shot learning setting.
%R 10.18653/v1/2022.findings-emnlp.250
%U https://aclanthology.org/2022.findings-emnlp.250
%U https://doi.org/10.18653/v1/2022.findings-emnlp.250
%P 3428-3435
Markdown (Informal)
[Knowledge-grounded Dialog State Tracking](https://aclanthology.org/2022.findings-emnlp.250) (Yu et al., Findings 2022)
ACL
- Dian Yu, Mingqiu Wang, Yuan Cao, Laurent El Shafey, Izhak Shafran, and Hagen Soltau. 2022. Knowledge-grounded Dialog State Tracking. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3428–3435, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.