# Weakly Supervised Semantic Parsing with Execution-based Spurious Program Filtering

arXiv: https://arxiv.org/abs/2311.01161 · HTML: https://mlsanity.com/paper/2311.01161/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 14 of 14)

- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Compositional Semantic Parsing on Semi-Structured Tables](https://mlsanity.com/paper/1508.00305/) (2015)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing](https://mlsanity.com/paper/2009.13845/) (2020)
- [Semantic Evaluation for Text-to-SQL with Distilled Test Suites](https://mlsanity.com/paper/2010.02840/) (2020)
- [Natural Language to Code Translation with Execution](https://mlsanity.com/paper/2204.11454/) (2022)
- [Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing](https://mlsanity.com/paper/1807.02322/) (2018)
- [Learning to Generalize from Sparse and Underspecified Rewards](https://mlsanity.com/paper/1902.07198/) (2019)
- [Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs](https://mlsanity.com/paper/1909.04165/) (2019)
- [Learning to Represent Programs with Property Signatures](https://mlsanity.com/paper/2002.09030/) (2020)
- [Weakly-supervised Semantic Parsing with Abstract Examples](https://mlsanity.com/paper/1711.05240/) (2017)
- [Macro Grammars and Holistic Triggering for Efficient Semantic Parsing](https://mlsanity.com/paper/1707.07806/) (2017)
- [Inferring Logical Forms From Denotations](https://mlsanity.com/paper/1606.06900/) (2016)
- [Enforcing Consistency in Weakly Supervised Semantic Parsing](https://mlsanity.com/paper/2107.05833/) (2021)

## Built on by (most cited first, 1 of 1)

- [Program Synthesis via Test-Time Transduction](https://mlsanity.com/paper/2509.17393/) (2025)

## Abstract (arXiv)

The problem of spurious programs is a longstanding challenge when training a semantic parser from weak supervision. To eliminate such programs that have wrong semantics but correct denotation, existing methods focus on exploiting similarities between examples based on domain-specific knowledge. In this paper, we propose a domain-agnostic filtering mechanism based on program execution results. Specifically, for each program obtained through the search process, we first construct a representation that captures the program's semantics as execution results under various inputs. Then, we run a majority vote on these representations to identify and filter out programs with significantly different semantics from the other programs. In particular, our method is orthogonal to the program search process so that it can easily augment any of the existing weakly supervised semantic parsing frameworks. Empirical evaluations on the Natural Language Visual Reasoning and WikiTableQuestions demonstrate that applying our method to the existing semantic parsers induces significantly improved performances.