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Neural Paraphrase Generation with Stacked Residual LSTM Networks

arXiv 1610.03098 ↗First posted 10 Oct 2016cs.CLcited by 52

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Abstract (arXiv)

In this paper, we propose a novel neural approach for paraphrase generation. Conventional para- phrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles. To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation. Our primary contribution is a stacked residual LSTM network, where we add residual connections between LSTM layers. This allows for efficient training of deep LSTMs. We evaluate our model and other state-of-the-art deep learning models on three different datasets: PPDB, WikiAnswers and MSCOCO. Evaluation results demonstrate that our model outperforms sequence to sequence, attention-based and bi- directional LSTM models on BLEU, METEOR, TER and an embedding-based sentence similarity metric.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Oct 2016.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 16; 2022: 16; 2023: 11; 2024: 5; 2025: 2; 2026: 1.

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  1. 2021GODIVA: Generating Open-DomaIn Videos from nAtural Descriptionscited by 254
  2. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  3. 2023DINOISER: Diffused Conditional Sequence Learning by Manipulating Noisescited by 50
  4. 2021Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairscited by 45
  5. 2022How Large Language Models are Transforming Machine-Paraphrased Plagiarismcited by 38
  6. 2024A Comprehensive Survey on Data Augmentationcited by 38
  7. 2022LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Taggingcited by 29
  8. 2022Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuningcited by 25
  9. 2022Hierarchical Sketch Induction for Paraphrase Generationcited by 21
  10. 2021A Review of Text Style Transfer using Deep Learningcited by 18
  11. 2021ConRPG: Paraphrase Generation using Contexts as Regularizercited by 13
  12. 2022Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priorscited by 13
38 more
  1. 2022Paraphrase Identification with Deep Learning: A Review of Datasets and Methodscited by 13
  2. 2021ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversationcited by 12
  3. 2019An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generationcited by 11
  4. 2023ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translationcited by 11
  5. 2021Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learningcited by 10
  6. 2021Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reorderingcited by 10
  7. 2022Prophet Attention: Predicting Attention with Future Attention for Image Captioningcited by 10
  8. 2022ExtraPhrase: Efficient Data Augmentation for Abstractive Summarizationcited by 8
  9. 2021Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approachcited by 7
  10. 2023Math Word Problem Solving by Generating Linguistic Variants of Problem Statementscited by 7
  11. 2021Contrastive Representation Learning for Exemplar-Guided Paraphrase Generationcited by 6
  12. 2021From Solving a Problem Boldly to Cutting the Gordian Knot: Idiomatic Text Generationcited by 5
  13. 2021Simulated annealing for optimization of graphs and sequencescited by 5
  14. 2023AskIt: Unified Programming Interface for Programming with Large Language Modelscited by 5
  15. 2025Fake News Detection After LLM Laundering: Measurement and Explanationcited by 4
  16. 2021Learning to Selectively Learn for Weakly-supervised Paraphrase Generationcited by 3
  17. 2022Entailment Relation Aware Paraphrase Generationcited by 3
  18. 2022N-gram Is Back: Residual Learning of Neural Text Generation with n-gram Language Modelcited by 3
  19. 2023Weakly Supervised Reasoning by Neuro-Symbolic Approachescited by 3
  20. 2021Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputscited by 2
  21. 2022Multi-Figurative Language Generationcited by 2
  22. 2023Emotion and Sentiment Guided Paraphrasingcited by 2
  23. 2023Fine-Tuning Language Models for Scientific Writing Supportcited by 2
  24. 2024Learning to Adapt to Low-Resource Paraphrase Generationcited by 2
  25. 2021MAPGN: MAsked Pointer-Generator Network for sequence-to-sequence pre-trainingcited by 1
  26. 2022Understanding Metrics for Paraphrasingcited by 1
  27. 2022Continuous Decomposition of Granularity for Neural Paraphrase Generationcited by 1
  28. 2023Language as a Latent Sequence: deep latent variable models for semi-supervised paraphrase generationcited by 1
  29. 2023Paraphrase Acquisition from Image Captionscited by 1
  30. 2023Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoisingcited by 1
  31. 2024Task-Oriented Paraphrase Analyticscited by 1
  32. 2024Effective Unsupervised Constrained Text Generation based on Perturbed Maskingcited by 1
  33. 2021Paraphrase Generation as Unsupervised Machine Translationcited by 0
  34. 2022Visual Information Guided Zero-Shot Paraphrase Generationcited by 0
  35. 2022Fine-Grained Emotional Paraphrasing along Emotion Gradientscited by 0
  36. 2022On-the-fly Denoising for Data Augmentation in Natural Language Understandingcited by 0
  37. 2023IdEALS: Idiomatic Expressions for Advancement of Language Skillscited by 0
  38. 2024Action Controlled Paraphrasingcited by 0

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