@inproceedings{cao-etal-2020-unsupervised-dual,
title = "Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing",
author = "Cao, Ruisheng and
Zhu, Su and
Yang, Chenyu and
Liu, Chen and
Ma, Rao and
Zhao, Yanbin and
Chen, Lu and
Yu, Kai",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.608",
doi = "10.18653/v1/2020.acl-main.608",
pages = "6806--6817",
abstract = "One daunting problem for semantic parsing is the scarcity of annotation. Aiming to reduce nontrivial human labor, we propose a two-stage semantic parsing framework, where the first stage utilizes an unsupervised paraphrase model to convert an unlabeled natural language utterance into the canonical utterance. The downstream naive semantic parser accepts the intermediate output and returns the target logical form. Furthermore, the entire training process is split into two phases: pre-training and cycle learning. Three tailored self-supervised tasks are introduced throughout training to activate the unsupervised paraphrase model. Experimental results on benchmarks Overnight and GeoGranno demonstrate that our framework is effective and compatible with supervised training.",
}
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<abstract>One daunting problem for semantic parsing is the scarcity of annotation. Aiming to reduce nontrivial human labor, we propose a two-stage semantic parsing framework, where the first stage utilizes an unsupervised paraphrase model to convert an unlabeled natural language utterance into the canonical utterance. The downstream naive semantic parser accepts the intermediate output and returns the target logical form. Furthermore, the entire training process is split into two phases: pre-training and cycle learning. Three tailored self-supervised tasks are introduced throughout training to activate the unsupervised paraphrase model. Experimental results on benchmarks Overnight and GeoGranno demonstrate that our framework is effective and compatible with supervised training.</abstract>
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%0 Conference Proceedings
%T Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing
%A Cao, Ruisheng
%A Zhu, Su
%A Yang, Chenyu
%A Liu, Chen
%A Ma, Rao
%A Zhao, Yanbin
%A Chen, Lu
%A Yu, Kai
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F cao-etal-2020-unsupervised-dual
%X One daunting problem for semantic parsing is the scarcity of annotation. Aiming to reduce nontrivial human labor, we propose a two-stage semantic parsing framework, where the first stage utilizes an unsupervised paraphrase model to convert an unlabeled natural language utterance into the canonical utterance. The downstream naive semantic parser accepts the intermediate output and returns the target logical form. Furthermore, the entire training process is split into two phases: pre-training and cycle learning. Three tailored self-supervised tasks are introduced throughout training to activate the unsupervised paraphrase model. Experimental results on benchmarks Overnight and GeoGranno demonstrate that our framework is effective and compatible with supervised training.
%R 10.18653/v1/2020.acl-main.608
%U https://aclanthology.org/2020.acl-main.608
%U https://doi.org/10.18653/v1/2020.acl-main.608
%P 6806-6817
Markdown (Informal)
[Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing](https://aclanthology.org/2020.acl-main.608) (Cao et al., ACL 2020)
ACL
- Ruisheng Cao, Su Zhu, Chenyu Yang, Chen Liu, Rao Ma, Yanbin Zhao, Lu Chen, and Kai Yu. 2020. Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6806–6817, Online. Association for Computational Linguistics.