mlsanityData to 7 Oct 2026

Controllable Paraphrase Generation with a Syntactic Exemplar

arXiv 1906.00565 ↗First posted 3 Jun 2019cs.CLcited by 44

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Abstract (arXiv)

Prior work on controllable text generation usually assumes that the controlled attribute can take on one of a small set of values known a priori. In this work, we propose a novel task, where the syntax of a generated sentence is controlled rather by a sentential exemplar. To evaluate quantitatively with standard metrics, we create a novel dataset with human annotations. We also develop a variational model with a neural module specifically designed for capturing syntactic knowledge and several multitask training objectives to promote disentangled representation learning. Empirically, the proposed model is observed to achieve improvements over baselines and learn to capture desirable characteristics.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2019.

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Citing papers per year: 2021: 12; 2022: 13; 2023: 12; 2024: 5; 2025: 2.

44 papers.

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  1. 2023Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defensecited by 401
  2. 2022A Survey on Retrieval-Augmented Text Generationcited by 157
  3. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  4. 2021Tailor: Generating and Perturbing Text with Semantic Controlscited by 67
  5. 2021Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairscited by 45
  6. 2023ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustnesscited by 41
  7. 2023Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systemscited by 37
  8. 2022Quality Controlled Paraphrase Generationcited by 31
  9. 2023Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasingcited by 31
  10. 2022PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generationcited by 30
  11. 2022Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuningcited by 25
  12. 2022CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generationcited by 23
32 more
  1. 2021Factorising Meaning and Form for Intent-Preserving Paraphrasingcited by 22
  2. 2022Hierarchical Sketch Induction for Paraphrase Generationcited by 21
  3. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  4. 2022Why is constrained neural language generation particularly challenging?cited by 19
  5. 2024Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challengescited by 18
  6. 2021Improving Formality Style Transfer with Context-Aware Rule Injectioncited by 13
  7. 2021ConRPG: Paraphrase Generation using Contexts as Regularizercited by 13
  8. 2021GenNI: Human-AI Collaboration for Data-Backed Text Generationcited by 11
  9. 2023Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representationcited by 10
  10. 2024Monotonic Paraphrasing Improves Generalization of Language Model Promptingcited by 10
  11. 2021Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communicationcited by 9
  12. 2022Improving Large-scale Paraphrase Acquisition and Generationcited by 8
  13. 2023Expository Text Generation: Imitate, Retrieve, Paraphrasecited by 8
  14. 2023Explicit Syntactic Guidance for Neural Text Generationcited by 8
  15. 2021Contrastive Representation Learning for Exemplar-Guided Paraphrase Generationcited by 6
  16. 2022Principled Paraphrase Generation with Parallel Corporacited by 5
  17. 2021Data-to-text Generation by Splicing Together Nearest Neighborscited by 4
  18. 2022Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representationscited by 4
  19. 2023Paraphrase Types for Generation and Detectioncited by 4
  20. 2024A Data-Driven Guided Decoding Mechanism for Diagnostic Captioningcited by 4
  21. 2021Learning to Selectively Learn for Weakly-supervised Paraphrase Generationcited by 3
  22. 2022Entailment Relation Aware Paraphrase Generationcited by 3
  23. 2022Exploiting Inductive Bias in Transformers for Unsupervised Disentanglement of Syntax and Semantics with VAEscited by 3
  24. 2025Syntactic Control of Language Models by Posterior Inferencecited by 3
  25. 2023Leveraging Large Language Models for Enhanced Product Descriptions in eCommercecited by 2
  26. 2023Adversarial Word Dilution as Text Data Augmentation in Low-Resource Regimecited by 1
  27. 2023A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase Generationcited by 1
  28. 2024Linguistically-Controlled Paraphrase Generationcited by 1
  29. 2021Paraphrase Generation as Unsupervised Machine Translationcited by 0
  30. 2023Text Fact Transfercited by 0
  31. 2024Action Controlled Paraphrasingcited by 0
  32. 2025BTC-SAM: Leveraging LLMs for Generation of Bias Test Cases for Sentiment Analysis Modelscited by 0