mlsanityData to 7 Oct 2026

Linguistically-Controlled Paraphrase Generation

arXiv 2410.24199 ↗First posted 31 Oct 2024cs.CLcited by 1

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Abstract (arXiv)

Controlled paraphrase generation produces paraphrases that preserve meaning while allowing precise control over linguistic attributes of the output. We introduce LingConv, an encoder-decoder framework that enables fine-grained control over 40 linguistic attributes in English. To improve reliability, we introduce a novel inference-time quality control mechanism that iteratively refines attribute embeddings to generate paraphrases that closely match target attributes without sacrificing semantic fidelity. LingConv reduces attribute error by up to 34% over existing models, with the quality control mechanism contributing an additional 14% improvement.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 37 of its 67 references that are in the map. First posted on arXiv 31 Oct 2024.

Builds on

37 of 67 references.

References · 37

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  4. 2021Convex optimizationcited by 2,479
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  7. 2017SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluationcited by 998
  8. 2019Plug and Play Language Models: A Simple Approach to Controlled Text Generationcited by 793
  9. 2021FUDGE: Controlled Text Generation With Future Discriminatorscited by 374
  10. 2018Adversarial Example Generation with Syntactically Controlled Paraphrase Networkscited by 282
  11. 2020Deep Learning for Text Style Transfer: A Surveycited by 161
  12. 2017Controlling Linguistic Style Aspects in Neural Language Generationcited by 147
25 more
  1. 2017Sentence Simplification with Deep Reinforcement Learningcited by 107
  2. 2021Controlled Text Generation as Continuous Optimization with Multiple Constraintscited by 70
  3. 2020Neural Syntactic Preordering for Controlled Paraphrase Generationcited by 56
  4. 2020Controllable Text Simplification with Explicit Paraphrasingcited by 53
  5. 2023Controlled Text Generation via Language Model Arithmeticcited by 51
  6. 2021Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairscited by 45
  7. 2019Controllable Paraphrase Generation with a Syntactic Exemplarcited by 44
  8. 2019Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representationcited by 34
  9. 2022On the Evaluation Metrics for Paraphrase Generationcited by 32
  10. 2022Quality Controlled Paraphrase Generationcited by 31
  11. 2022Composable Text Controls in Latent Space with ODEscited by 28
  12. 2022Controllable Text Generation via Probability Density Estimation in the Latent Spacecited by 27
  13. 2023BOLT: Fast Energy-based Controlled Text Generation with Tunable Biasescited by 24
  14. 2021Factorising Meaning and Form for Intent-Preserving Paraphrasingcited by 22
  15. 2022Hierarchical Sketch Induction for Paraphrase Generationcited by 21
  16. 2019Controlled Text Generation for Data Augmentation in Intelligent Artificial Agentscited by 20
  17. 2023Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rankcited by 12
  18. 2023ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translationcited by 11
  19. 2020Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classificationcited by 10
  20. 2021Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reorderingcited by 10
  21. 2023An Invariant Learning Characterization of Controlled Text Generationcited by 9
  22. 2023Prompt-based Learning for Text Readability Assessmentcited by 8
  23. 2023Extrapolative Controlled Sequence Generation via Iterative Refinementcited by 7
  24. 2022Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representationscited by 4
  25. 2023Ling-CL: Understanding NLP Models through Linguistic Curriculacited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Implicit Manipulation for Skill Selection in LLM Agents with Semantic Matchingcited by 0