# Generating Logical Forms from Graph Representations of Text and Entities

arXiv: https://arxiv.org/abs/1905.08407 · HTML: https://mlsanity.com/paper/1905.08407/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2019.

## Built on by (most cited first, 10 of 10)

- [WebFormer: The Web-page Transformer for Structure Information Extraction](https://mlsanity.com/paper/2202.00217/) (2022)
- [Representations for Question Answering from Documents with Tables and Text](https://mlsanity.com/paper/2101.10573/) (2021)
- [Zero-Shot Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2104.07554/) (2021)
- [On The Ingredients of an Effective Zero-shot Semantic Parser](https://mlsanity.com/paper/2110.08381/) (2021)
- [Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2109.00269/) (2021)
- [Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks](https://mlsanity.com/paper/2410.22296/) (2024)
- [Text2Chart: A Multi-Staged Chart Generator from Natural Language Text](https://mlsanity.com/paper/2104.04584/) (2021)
- [Graph-Based Decoding for Task Oriented Semantic Parsing](https://mlsanity.com/paper/2109.04587/) (2021)
- [ShopTalk: A System for Conversational Faceted Search](https://mlsanity.com/paper/2109.00702/) (2021)
- [Combining Improvements for Exploiting Dependency Trees in Neural Semantic Parsing](https://mlsanity.com/paper/2112.13179/) (2021)

## Abstract (arXiv)

Structured information about entities is critical for many semantic parsing tasks. We present an approach that uses a Graph Neural Network (GNN) architecture to incorporate information about relevant entities and their relations during parsing. Combined with a decoder copy mechanism, this approach provides a conceptually simple mechanism to generate logical forms with entities. We demonstrate that this approach is competitive with the state-of-the-art across several tasks without pre-training, and outperforms existing approaches when combined with BERT pre-training.