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# Who cites «ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation»

19 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/huang-2023-paraamr-large-scale-syntactically-diverse/)

1. [Paraphrasing as Zero-shot Translation with Feature-guided Diversity Enhancement](https://aclanthology.org/2026.acl-long.783/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [Ambiguity and Disagreement in Abstract Meaning Representation](https://aclanthology.org/2025.comedi-1.14/) *— 2025 · Proceedings of Context and Meaning: Navigating Disagreements in NLP Annotation*
3. [Enhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked Data](https://arxiv.org/abs/2506.02018) *— 2025 · arXiv:2506.02018*
4. [LingConv: An Interactive Toolkit for Controlled Paraphrase Generation with Linguistic Attribute Control](https://aclanthology.org/2025.emnlp-demos.4/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
5. [Linguistically-Controlled Paraphrase Generation](https://aclanthology.org/2025.findings-emnlp.1137/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
6. [PAM: Paraphrase AMR-Centric Evaluation Metric](https://aclanthology.org/2025.findings-acl.879/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
7. [Reassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based](https://aclanthology.org/2025.insights-1.3/) *— 2025 · The Sixth Workshop on Insights from Negative Results in NLP*
8. [Reassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based Encoding](https://arxiv.org/abs/2505.08504) *— 2025 · arXiv:2505.08504*
9. [Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints](https://aclanthology.org/2025.acl-short.79/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
10. [Survey of Abstract Meaning Representation: Then, Now, Future](https://md.mova.elamur.ai/free/en/docs/papers/mansouri-2025-survey-abstract-meaning-representation-then/) *— our card · 2025 · arXiv:2505.03229*
11. [VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models](https://aclanthology.org/2025.naacl-long.311/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
12. [A Survey of AMR Applications](https://aclanthology.org/2024.emnlp-main.390/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
13. [A Survey of Meaning Representations – From Theory to Practical Utility](https://aclanthology.org/2024.naacl-long.159/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
14. [How Far Can We Extract Diverse Perspectives from Large Language Models?](https://aclanthology.org/2024.emnlp-main.306/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
15. [Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Model](https://aclanthology.org/2024.naacl-long.250/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
16. [The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text](https://aclanthology.org/2024.findings-naacl.228/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
17. [Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing](https://arxiv.org/abs/2305.16635) *— 2023 · arXiv:2305.16635*
18. fung-2026-emphasizing-commendable-study-homogenized-transitive
19. qin-2025-data-drives-unstable-hierarchical-generalization
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