# Learning Structured Natural Language Representations for Semantic Parsing

arXiv: https://arxiv.org/abs/1704.08387 · HTML: https://mlsanity.com/paper/1704.08387/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2017.

## Built on by (most cited first, 15 of 15)

- [Program Transfer for Answering Complex Questions over Knowledge Bases](https://mlsanity.com/paper/2110.05743/) (2021)
- [Formal Specifications from Natural Language](https://mlsanity.com/paper/2206.01962/) (2022)
- [Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing](https://mlsanity.com/paper/2109.10540/) (2021)
- [The Power of Prompt Tuning for Low-Resource Semantic Parsing](https://mlsanity.com/paper/2110.08525/) (2021)
- [GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation](https://mlsanity.com/paper/2205.12078/) (2022)
- [Automated Attack Synthesis by Extracting Finite State Machines from Protocol Specification Documents](https://mlsanity.com/paper/2202.09470/) (2022)
- [Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL](https://mlsanity.com/paper/2112.08735/) (2021)
- [Scene Graph Modification as Incremental Structure Expanding](https://mlsanity.com/paper/2209.09093/) (2022)
- [TreePiece: Faster Semantic Parsing via Tree Tokenization](https://mlsanity.com/paper/2303.17161/) (2023)
- [VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering](https://mlsanity.com/paper/2307.03130/) (2023)
- [Warmup Generations: A Task-Agnostic Approach for Guiding Sequence-to-Sequence Learning with Unsupervised Initial State Generation](https://mlsanity.com/paper/2502.12304/) (2025)
- [Toward a Neural Semantic Parsing System for EHR Question Answering](https://mlsanity.com/paper/2211.04569/) (2022)
- [SLFNet: Generating Semantic Logic Forms from Natural Language Using Semantic Probability Graphs](https://mlsanity.com/paper/2403.19936/) (2024)
- [Semantic Parsing with Candidate Expressions for Knowledge Base Question Answering](https://mlsanity.com/paper/2410.00414/) (2024)
- [SemSketches-2021: experimenting with the machine processing of the pilot semantic sketches corpus](https://mlsanity.com/paper/2505.17704/) (2025)

## Abstract (arXiv)

We introduce a neural semantic parser that converts natural language utterances to intermediate representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We obtain competitive results on various datasets. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.