# Inferring Logical Forms From Denotations

arXiv: https://arxiv.org/abs/1606.06900 · HTML: https://mlsanity.com/paper/1606.06900/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2016.

## Built on by (most cited first, 6 of 6)

- [From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding](https://mlsanity.com/paper/2106.06228/) (2021)
- [Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL](https://mlsanity.com/paper/2205.12422/) (2022)
- [Toward Code Generation: A Survey and Lessons from Semantic Parsing](https://mlsanity.com/paper/2105.03317/) (2021)
- [ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision](https://mlsanity.com/paper/2210.07017/) (2022)
- [Weakly Supervised Semantic Parsing with Execution-based Spurious Program Filtering](https://mlsanity.com/paper/2311.01161/) (2023)
- [Semantic Parsing with Candidate Expressions for Knowledge Base Question Answering](https://mlsanity.com/paper/2410.00414/) (2024)

## Abstract (arXiv)

A core problem in learning semantic parsers from denotations is picking out consistent logical forms--those that yield the correct denotation--from a combinatorially large space. To control the search space, previous work relied on restricted set of rules, which limits expressivity. In this paper, we consider a much more expressive class of logical forms, and show how to use dynamic programming to efficiently represent the complete set of consistent logical forms. Expressivity also introduces many more spurious logical forms which are consistent with the correct denotation but do not represent the meaning of the utterance. To address this, we generate fictitious worlds and use crowdsourced denotations on these worlds to filter out spurious logical forms. On the WikiTableQuestions dataset, we increase the coverage of answerable questions from 53.5% to 76%, and the additional crowdsourced supervision lets us rule out 92.1% of spurious logical forms.