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A Continuously Growing Dataset of Sentential Paraphrases

arXiv 1708.00391 ↗First posted 1 Aug 2017cs.CLcited by 72

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Abstract (arXiv)

A major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets through shared URLs. The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase identification algorithms in the previous work. We present the largest human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification. In addition, we show that more than 30,000 new sentential paraphrases can be easily and continuously captured every month at ~70% precision, and demonstrate their utility for downstream NLP tasks through phrasal paraphrase extraction. We make our code and data freely available.

Key facts. Cited by 72 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Aug 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 22; 2022: 20; 2023: 6; 2024: 14; 2025: 2; 2026: 7.

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  1. 2022MTEB: Massive Text Embedding Benchmarkcited by 623
  2. 2021Active Learning by Acquiring Contrastive Examplescited by 154
  3. 2021TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learningcited by 116
  4. 2022Describing Differences between Text Distributions with Natural Languagecited by 54
  5. 2021Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Levelcited by 34
  6. 2022On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliencycited by 31
  7. 2023Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Modelscited by 29
  8. 2021ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generationcited by 28
  9. 2021Bayesian Attention Belief Networkscited by 25
  10. 2021Using Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Taskscited by 22
  11. 2022DABERT: Dual Attention Enhanced BERT for Semantic Matchingcited by 21
  12. 2021Improving Paraphrase Detection with the Adversarial Paraphrasing Taskcited by 19
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  1. 2022Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusioncited by 18
  2. 2022Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Frameworkcited by 18
  3. 2024Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challengescited by 18
  4. 2021Identifying Machine-Paraphrased Plagiarismcited by 16
  5. 2021Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixingcited by 16
  6. 2022Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue Systemcited by 16
  7. 2021Neural semi-Markov CRF for Monolingual Word Alignmentcited by 15
  8. 2021Alignment Attention by Matching Key and Query Distributionscited by 14
  9. 2021ConRPG: Paraphrase Generation using Contexts as Regularizercited by 13
  10. 2022Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priorscited by 13
  11. 2023Paraphrase Detection: Human vs. Machine Contentcited by 12
  12. 2019An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generationcited by 11
  13. 2021Finnish Paraphrase Corpuscited by 10
  14. 2022Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learningcited by 10
  15. 2024Comateformer: Combined Attention Transformer for Semantic Sentence Matchingcited by 9
  16. 2022Improving Large-scale Paraphrase Acquisition and Generationcited by 8
  17. 2024ParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversitycited by 8
  18. 2022Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimatescited by 7
  19. 2021Semi-supervised Interactive Intent Labelingcited by 5
  20. 2021Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuningcited by 5
  21. 2021Simulated annealing for optimization of graphs and sequencescited by 5
  22. 2022A Data Cartography based MixUp for Pre-trained Language Modelscited by 5
  23. 2022Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLUcited by 5
  24. 2026CSRv2: Unlocking Ultra-Sparse Embeddingscited by 5
  25. 2022Revision for Concision: A Constrained Paraphrase Generation Taskcited by 4
  26. 2022Relational Sentence Embedding for Flexible Semantic Matchingcited by 4
  27. 2023Paraphrase Types for Generation and Detectioncited by 4
  28. 2026The Embedder's Dilemma: LLMs Are Better, but at What Cost?cited by 4
  29. 2021On Length Divergence Bias in Textual Matching Modelscited by 3
  30. 2021Learning to Selectively Learn for Weakly-supervised Paraphrase Generationcited by 3
  31. 2021Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddingscited by 3
  32. 2022Decay No More: A Persistent Twitter Dataset for Learning Social Meaningcited by 3
  33. 2022Pathologies of Pre-trained Language Models in Few-shot Fine-tuningcited by 3
  34. 2024Finding a Needle in the Adversarial Haystack: A Targeted Paraphrasing Approach For Uncovering Edge Cases with Minimal Distribution Distortioncited by 3
  35. 2024Parameter Efficient Diverse Paraphrase Generation Using Sequence-Level Knowledge Distillationcited by 3
  36. 2024VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Modelscited by 3
  37. 2021LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matchingcited by 2
  38. 2021Extracting and filtering paraphrases by bridging natural language inference and paraphrasingcited by 2

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