# Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing

arXiv: https://arxiv.org/abs/2005.13485 · HTML: https://mlsanity.com/paper/2005.13485/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 20 of 20)

- [ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser](https://mlsanity.com/paper/2104.04689/) (2021)
- [On the Evaluation Metrics for Paraphrase Generation](https://mlsanity.com/paper/2202.08479/) (2022)
- [LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification](https://mlsanity.com/paper/2106.01649/) (2021)
- [BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset](https://mlsanity.com/paper/2210.05109/) (2022)
- [From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding](https://mlsanity.com/paper/2106.06228/) (2021)
- [Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL](https://mlsanity.com/paper/2106.02282/) (2021)
- [GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation](https://mlsanity.com/paper/2205.12078/) (2022)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Privacy-Preserving Domain Adaptation of Semantic Parsers](https://mlsanity.com/paper/2212.10520/) (2022)
- [Paraphrase Detection: Human vs. Machine Content](https://mlsanity.com/paper/2303.13989/) (2023)
- [Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2109.00269/) (2021)
- [Pre-training Universal Language Representation](https://mlsanity.com/paper/2105.14478/) (2021)
- [Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation](https://mlsanity.com/paper/2205.08675/) (2022)
- [Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsing](https://mlsanity.com/paper/2210.01425/) (2022)
- [Revision for Concision: A Constrained Paraphrase Generation Task](https://mlsanity.com/paper/2210.14257/) (2022)
- [Harnessing Knowledge and Reasoning for Human-Like Natural Language Generation: A Brief Review](https://mlsanity.com/paper/2212.03747/) (2022)
- [Semantic-aware Contrastive Learning for More Accurate Semantic Parsing](https://mlsanity.com/paper/2301.07919/) (2023)
- [Semantic Parsing with Candidate Expressions for Knowledge Base Question Answering](https://mlsanity.com/paper/2410.00414/) (2024)
- [MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models](https://mlsanity.com/paper/2508.17444/) (2025)
- [Action Controlled Paraphrasing](https://mlsanity.com/paper/2405.11277/) (2024)

## Abstract (arXiv)

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.