---
title: "Grounded Adaptation for Zero-shot Executable Semantic Parsing"
description: "The authors present GAZP, a model-agnostic method that adapts a parser to such unseen… 26 references, cited by 50 papers here."
authors: ["Victor Zhong", "Mike Lewis", "Sida I. Wang", "Luke Zettlemoyer"]
year: 2020
venue: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/2020.emnlp-main.558/"
doi: "10.18653/v1/2020.emnlp-main.558"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhong-2020-grounded-adaptation-zero-shot-executable/"
---
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# Grounded Adaptation for Zero-shot Executable Semantic Parsing

**Authors:** [Victor Zhong](https://md.research.elamur.ai/free/en/authors/victor-zhong/), [Mike Lewis](https://md.research.elamur.ai/free/en/authors/mike-lewis/), [Sida I. Wang](https://md.research.elamur.ai/free/en/authors/sida-i-wang/) and [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/) · **Year:** 2020 · **Venue:** [Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2020](https://md.research.elamur.ai/free/en/venues/emnlp/2020/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.emnlp-main.558/) · [PDF](https://aclanthology.org/2020.emnlp-main.558.pdf) · [DOI 10.18653/v1/2020.emnlp-main.558](https://doi.org/10.18653/v1/2020.emnlp-main.558) · [OpenAlex](https://openalex.org/W3103962261)

**Citations:** 85 citations (OpenAlex, 2026-10-14) · [50](https://md.research.elamur.ai/free/en/papers/zhong-2020-grounded-adaptation-zero-shot-executable/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Executable semantic parsers, such as text-to-SQL systems, tend to fail when deployed on a database they never encountered during training, and labelled examples for each new schema are rarely available. The authors present GAZP, a model-agnostic method that adapts a parser to such unseen environments without manual annotation. Two models are first trained on the original data. The first is a forward parser that uses BERT to cross-encode the question with each table schema, then applies BiLSTMs and pointer decoders for query templates and values. The second is a backward generator that turns SQL into questions. In the target database, SQL queries are sampled from coarse templates weighted by training-set frequency, converted into questions, and parsed again. Only pairs whose re-parse agrees with the sampled query, by execution result or exact match, are kept. Retraining on the original data plus the filtered synthetic data improves both logical form and execution accuracy on the zero-shot Spider, Sparc and CoSQL benchmarks. The results suggest that verified synthetic data built in the deployment environment is a cheap and effective route to domain transfer.

## Key points
- Evaluated on three language-to-SQL zero-shot tasks with unseen test databases: Spider, Sparc and CoSQL. GAZP improves the baseline parser's logical form and execution accuracy on all three.
- Query sampling abstracts training queries into coarse templates with typed column and value slots and removes JOINs, which are later restored deterministically. Queries that are invalid or return an empty result are discarded.
- The cycle-consistency filter discards roughly 60% of the synthesized examples. Keeping all of them ("nocycle") clearly hurts performance. String-match ("EM") consistency performs close to execution-based consistency but usually gives lower execution accuracy.
- Adaptation quality grows with the amount of synthesized data, with gains levelling off after about 30–40k examples.
- GAZP beats data augmentation carried out in the training environment. It can be applied to any parser for which a backward generator can be trained and logical-form equivalence can be checked.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Victor Zhong, Mike Lewis, Sida I. Wang and Luke Zettlemoyer. 2020. Grounded Adaptation for Zero-shot Executable Semantic Parsing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhong2020grounded,
  title = {{Grounded Adaptation for Zero-shot Executable Semantic Parsing}},
  author = {Victor Zhong, Mike Lewis, Sida I. Wang and Luke Zettlemoyer},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.558},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.558}
}
```

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*Updated 24 October 2026*