# A Deep Architecture for Semantic Parsing

arXiv: https://arxiv.org/abs/1404.7296 · HTML: https://mlsanity.com/paper/1404.7296/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2014.

## Built on by (most cited first, 2 of 2)

- [Detecting Conceptual Abstraction in LLMs](https://mlsanity.com/paper/2404.15848/) (2024)
- [Deep Apprenticeship Learning for Playing Games](https://mlsanity.com/paper/2205.07959/) (2022)

## Abstract (arXiv)

Many successful approaches to semantic parsing build on top of the syntactic analysis of text, and make use of distributional representations or statistical models to match parses to ontology-specific queries. This paper presents a novel deep learning architecture which provides a semantic parsing system through the union of two neural models of language semantics. It allows for the generation of ontology-specific queries from natural language statements and questions without the need for parsing, which makes it especially suitable to grammatically malformed or syntactically atypical text, such as tweets, as well as permitting the development of semantic parsers for resource-poor languages.