# Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing

arXiv: https://arxiv.org/abs/1809.00773 · HTML: https://mlsanity.com/paper/1809.00773/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2018.

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

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- [On Robustness of Neural Semantic Parsers](https://mlsanity.com/paper/2102.01563/) (2021)
- [From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding](https://mlsanity.com/paper/2106.06228/) (2021)
- [GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation](https://mlsanity.com/paper/2205.12078/) (2022)
- [Discrete Graph Auto-Encoder](https://mlsanity.com/paper/2306.07735/) (2023)
- [Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers](https://mlsanity.com/paper/2109.05186/) (2021)
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- [Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsing](https://mlsanity.com/paper/2210.01425/) (2022)
- [Modeling Global Semantics for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2101.01510/) (2021)
- [A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation](https://mlsanity.com/paper/2412.15582/) (2024)
- [Semantic-aware Contrastive Learning for More Accurate Semantic Parsing](https://mlsanity.com/paper/2301.07919/) (2023)
- [Hierarchical Discrete Flow Matching for Graph Generation](https://mlsanity.com/paper/2604.00236/) (2026)
- [GraphTune: A Learning-based Graph Generative Model with Tunable Structural Features](https://mlsanity.com/paper/2201.11494/) (2022)
- [Semantic Parsing for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2401.06772/) (2023)
- [Semantic Parsing with Candidate Expressions for Knowledge Base Question Answering](https://mlsanity.com/paper/2410.00414/) (2024)
- [Combining Improvements for Exploiting Dependency Trees in Neural Semantic Parsing](https://mlsanity.com/paper/2112.13179/) (2021)
- [Perspectives on neural proof nets](https://mlsanity.com/paper/2211.04141/) (2022)
- [Generating Large Semi-Synthetic Graphs of Any Size](https://mlsanity.com/paper/2507.02166/) (2025)

## Abstract (arXiv)

This paper proposes a neural semantic parsing approach -- Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases. Secondly, by leveraging the powerful representation learning and prediction ability of neural network models, we propose a RNN model which can effectively map sentences to action sequences for semantic graph generation. Experiments show that our method achieves state-of-the-art performance on OVERNIGHT dataset and gets competitive performance on GEO and ATIS datasets.