Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing

• URL: http://arxiv.org/abs/2005.13485v3
• Date: Tue, 22 Dec 2020 11:45:03 GMT
• Title: Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing
• Authors: Ruisheng Cao, Su Zhu, Chenyu Yang, Chen Liu, Rao Ma, Yanbin Zhao, Lu Chen and Kai Yu
• Abstract summary: We propose a two-stage semantic parsing framework to reduce nontrivial human labor. The first stage utilizes an unsupervised paraphrase model to convert an unlabeled natural language utterance into a canonical utterance. The downstream naive semantic accepts the intermediate output and returns the target logical form.
• Score: 41.345662724584884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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