# Learning Executable Semantic Parsers for Natural Language Understanding

arXiv: https://arxiv.org/abs/1603.06677 · HTML: https://mlsanity.com/paper/1603.06677/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2016.

## Built on by (most cited first, 21 of 21)

- [PAL: Program-aided Language Models](https://mlsanity.com/paper/2211.10435/) (2022)
- [Complexity-Based Prompting for Multi-Step Reasoning](https://mlsanity.com/paper/2210.00720/) (2022)
- [KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers](https://mlsanity.com/paper/2106.11455/) (2021)
- [Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning](https://mlsanity.com/paper/2107.02794/) (2021)
- [Leveraging Language to Learn Program Abstractions and Search Heuristics](https://mlsanity.com/paper/2106.11053/) (2021)
- [Open-Ended Instructable Embodied Agents with Memory-Augmented Large Language Models](https://mlsanity.com/paper/2310.15127/) (2023)
- [Communicating Natural Programs to Humans and Machines](https://mlsanity.com/paper/2106.07824/) (2021)
- [AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries](https://mlsanity.com/paper/2406.19073/) (2024)
- [RuleBert: Teaching Soft Rules to Pre-trained Language Models](https://mlsanity.com/paper/2109.13006/) (2021)
- [Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning](https://mlsanity.com/paper/2106.03921/) (2021)
- [Zero-Shot Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2104.07554/) (2021)
- [Towards Socially Intelligent Agents with Mental State Transition and Human Utility](https://mlsanity.com/paper/2103.07011/) (2021)
- [Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA](https://mlsanity.com/paper/2311.07850/) (2023)
- [Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing](https://mlsanity.com/paper/2305.15338/) (2023)
- [Symbolic Planning and Code Generation for Grounded Dialogue](https://mlsanity.com/paper/2310.17140/) (2023)
- [Compress the Context, Keep the Commitments: A Formal Framework for Verifiable LLM Context Compression](https://mlsanity.com/paper/2605.17304/) (2026)
- [Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2307.04096/) (2023)
- [Jackal: A Real-World Execution-Based Benchmark Evaluating Large Language Models on Text-to-JQL Tasks](https://mlsanity.com/paper/2509.23579/) (2025)
- [Trust in Language Grounding: a new AI challenge for human-robot teams](https://mlsanity.com/paper/2209.02066/) (2022)
- [An Inference Approach To Question Answering Over Knowledge Graphs](https://mlsanity.com/paper/2112.11070/) (2021)
- [Meta Semantics: Towards better natural language understanding and reasoning](https://mlsanity.com/paper/2304.10663/) (2023)

## Abstract (arXiv)

For building question answering systems and natural language interfaces, semantic parsing has emerged as an important and powerful paradigm. Semantic parsers map natural language into logical forms, the classic representation for many important linguistic phenomena. The modern twist is that we are interested in learning semantic parsers from data, which introduces a new layer of statistical and computational issues. This article lays out the components of a statistical semantic parser, highlighting the key challenges. We will see that semantic parsing is a rich fusion of the logical and the statistical world, and that this fusion will play an integral role in the future of natural language understanding systems.