---
title: "Who cites Factorising AMR generation through syntax"
description: "The papers of the Elamur Research library that cite «Factorising AMR generation through syntax», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/cao-2019-factorising-amr-generation-through-syntax/cited-by/"
---
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# Who cites «Factorising AMR generation through syntax»

12 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/cao-2019-factorising-amr-generation-through-syntax/)

1. [Using syntax for the semantic representation of sentences](https://aclanthology.org/2026.slide-1.15/) *— 2026 · Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)*
2. [ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation](https://md.research.elamur.ai/free/en/papers/huang-2023-paraamr-large-scale-syntactically-diverse/) *— our card · 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [Exploring a POS-based Two-stage Approach for Improving Low-Resource AMR-to-Text Generation](https://aclanthology.org/2022.gem-1.49/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
4. [A Human Evaluation of AMR-to-English Generation Systems](https://md.research.elamur.ai/free/en/papers/manning-2020-human-evaluation-amr-english-generation/) *— our card · 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
5. [GPT-too: A Language-Model-First Approach for AMR-to-Text Generation](https://aclanthology.org/2020.acl-main.167/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
6. [Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation](https://aclanthology.org/2020.emnlp-main.169/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
7. [Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks](https://aclanthology.org/2020.acl-main.67/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
8. [Online Back-Parsing for AMR-to-Text Generation](https://aclanthology.org/2020.emnlp-main.92/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
9. [Structural Information Preserving for Graph-to-Text Generation](https://aclanthology.org/2020.acl-main.712/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
10. [融合目标端句法的AMR-to-Text生成(AMR-to-Text Generation with Target Syntax)](https://aclanthology.org/2020.ccl-1.16/) *— 2020 · Proceedings of the 19th Chinese National Conference on Computational Linguistics*
11. [Enhancing AMR-to-Text Generation with Dual Graph Representations](https://aclanthology.org/D19-1314/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
12. [Modeling Graph Structure in Transformer for Better AMR-to-Text Generation](https://aclanthology.org/D19-1548/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*