mlsanityData to 14 Oct 2026

ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation

arXiv 2305.16585 ↗First posted 26 May 2023cs.CLcited by 11

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Abstract (arXiv)

Paraphrase generation is a long-standing task in natural language processing (NLP). Supervised paraphrase generation models, which rely on human-annotated paraphrase pairs, are cost-inefficient and hard to scale up. On the other hand, automatically annotated paraphrase pairs (e.g., by machine back-translation), usually suffer from the lack of syntactic diversity -- the generated paraphrase sentences are very similar to the source sentences in terms of syntax. In this work, we present ParaAMR, a large-scale syntactically diverse paraphrase dataset created by abstract meaning representation back-translation. Our quantitative analysis, qualitative examples, and human evaluation demonstrate that the paraphrases of ParaAMR are syntactically more diverse compared to existing large-scale paraphrase datasets while preserving good semantic similarity. In addition, we show that ParaAMR can be used to improve on three NLP tasks: learning sentence embeddings, syntactically controlled paraphrase generation, and data augmentation for few-shot learning. Our results thus showcase the potential of ParaAMR for improving various NLP applications.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 36 of its 71 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

36 of 71 references.

References · 36

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  3. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  4. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
  5. 2018Adversarial Example Generation with Syntactically Controlled Paraphrase Networkscited by 286
  6. 2018QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehensioncited by 164
  7. 2020Investigating Pretrained Language Models for Graph-to-Text Generationcited by 117
  8. 2018Graph-to-Sequence Learning using Gated Graph Neural Networkscited by 61
  9. 2017Neural AMR: Sequence-to-Sequence Models for Parsing and Generationcited by 56
  10. 2018A Graph-to-Sequence Model for AMR-to-Text Generationcited by 56
  11. 2020Neural Syntactic Preordering for Controlled Paraphrase Generationcited by 56
  12. 2017A Deep Generative Framework for Paraphrase Generationcited by 54
24 more
  1. 2016Neural Paraphrase Generation with Stacked Residual LSTM Networkscited by 52
  2. 2019AMR Parsing as Sequence-to-Graph Transductioncited by 50
  3. 2020AMR Parsing via Graph-Sequence Iterative Inferencecited by 47
  4. 2020Syntax-guided Controlled Generation of Paraphrasescited by 47
  5. 2021Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairscited by 45
  6. 2019ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translationcited by 38
  7. 2016An Incremental Parser for Abstract Meaning Representationcited by 34
  8. 2019Broad-Coverage Semantic Parsing as Transductioncited by 32
  9. 2019Decomposable Neural Paraphrase Generationcited by 30
  10. 2018Deep Graph Convolutional Encoders for Structured Data to Text Generationcited by 27
  11. 2019Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learningcited by 25
  12. 2022Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuningcited by 25
  13. 2021HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledgecited by 22
  14. 2019Structural Neural Encoders for AMR-to-text Generationcited by 19
  15. 2019Unsupervised Paraphrasing without Translationcited by 19
  16. 2019Syntax-Infused Variational Autoencoder for Text Generationcited by 19
  17. 2017Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representationscited by 18
  18. 2016Joint Copying and Restricted Generation for Paraphrasecited by 17
  19. 2017AMR Parsing using Stack-LSTMscited by 15
  20. 2017Addressing the Data Sparsity Issue in Neural AMR Parsingcited by 11
  21. 2021Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approachcited by 7
  22. 2018Factorising AMR generation through syntaxcited by 4
  23. 2018A Transition-based Algorithm for Unrestricted AMR Parsingcited by 4
  24. 2022Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representationscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 5; 2026: 1.

11 papers.

  1. 2023The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Textcited by 100
  2. 2025The Homogenizing Effect of Large Language Models on Human Expression and Thoughtcited by 35
  3. 2023How Far Can We Extract Diverse Perspectives from Large Language Models?cited by 22
  4. 2024Sometimes I am a Tree: Data Drives Unstable Hierarchical Generalizationcited by 10
  5. 2024VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Modelscited by 3
  6. 2025A Systematic Review of Open Datasets Used in Text-to-Image (T2I) Gen AI Model Safetycited by 2
  7. 2025Survey of Abstract Meaning Representation: Then, Now, Futurecited by 2
  8. 2024Linguistically-Controlled Paraphrase Generationcited by 1
  9. 2025Reassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based Encodingcited by 0
  10. 2025Enhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked Datacited by 0
  11. 2026Hallucination-R1: Robustness-Oriented Paraphrase Generation for Factual Consistencycited by 0