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Paraphrases are texts that convey the same meaning while using different words or sentence structures.
Zero-Shot Paraphrase Generation with Multilingual Language Models
Guo, Y.; Liao, Y.; Jiang, X.; Zhang, Q.; Zhang, Y.; and Liu, Q. 2019 · 1911
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Estimating the Reliability, Systematic Error and Random Error of Interval Data
Krippendorff, K. 1970 · 1970
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Projected Newton Methods for Optimization Problems with Simple Constraints
Bertsekas, D. P. 1982 · 1982
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Bleu: a Method for Automatic Evaluation of Machine Translation
Papineni, K.; Roukos, S.; Ward, T.; and Zhu, W.-J. 2002 · 2002
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Open subtitles paraphrase corpus for six languages
Creutz, M. 2019 · 2005
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A Study of Translation Edit Rate with Targeted Human Annotation
Snover, M.; Dorr, B.; Schwartz, R.; Micciulla, L.; and Makhoul, J. 2006 · 2006
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Explaining and Harnessing Adversarial Examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2015 · 2015
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Joint Copying and Restricted Generation for Paraphrase
Cao, Z.; Luo, C.; Li, W.; and Li, S. 2017 · 2017
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Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
Miyato, T.; ichi Maeda, S.; Koyama, M.; and Ishii, S. 2018 · 2018
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Cross-lingual Language Model Pretraining
Conneau, A.; and Lample, G. 2019 · 2019
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Multilingual Whispers: Generating Paraphrases with Translation
Federmann, C.; Elachqar, O.; and Quirk, C. 2019 · 2019
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ACL 2019 Fourth Conference on Machine Translation (WMT19), Shared Task: Machine Translation of News
Foundation, W. 2019 · 2019
Cited alongside, same era.
Improving the Robustness of Question Answering Systems to Question Paraphrasing
Gan, W. C.; and Ng, H. T. 2019 · 2019
Cited alongside, same era.
Parameter-Efficient Transfer Learning for NLP
Houlsby, N.; Giurgiu, A.; Jastrzebski, S.; Morrone, B.; De Laroussilhe, Q.; Gesmundo, A.; Attariyan, M.; and Gelly, S. 2019 · 2019
Cited alongside, same era.
Adversarial training for free!
Shafahi, A.; Najibi, M.; Ghiasi, M. A.; Xu, Z.; Dickerson, J.; Studer, C.; Davis, L. S.; Taylor, G.; and Goldstein, T. 2019 · 2019
Cited alongside, same era.
PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification
Yang, Y.; Zhang, Y.; Tar, C.; and Baldridge, J. 2019 · 2019
Cited alongside, same era.
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
BERTScore: Evaluating Text Generation with BERT
Zhang, T.; Kishore, V.; Wu, F.; Weinberger, K. Q.; and Artzi, Y. 2020 · 2020
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Factorising Meaning and Form for Intent-Preserving Paraphrasing
Hosking, T.; and Lapata, M. 2021 · 2021
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LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
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Kaushik, P.; Gain, A.; Kortylewski, A.; and Yuille, A. 2021 · 2021
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The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B.; Al-Rfou, R.; and Constant, N. 2021 · 2021
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Zhang, D.; Zhang, T.; Lu, Y.; Zhu, Z.; and Dong, B. 2019 · 2019
Cited alongside, same era.
Data for the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE)
Duolingo. 2020 · 2020
Cited alongside, same era.
Human-Paraphrased References Improve Neural Machine Translation
Freitag, M.; Foster, G.; Grangier, D.; and Cherry, C. 2020 · 2020
Cited alongside, same era.
Pre-training via Paraphrasing
Lewis, M.; Ghazvininejad, M.; Ghosh, G.; Aghajanyan, A.; Wang, S.; and Zettlemoyer, L. 2020 · 2020
Cited alongside, same era.
Adversarial Training for Commonsense Inference
Pereira, L.; Liu, X.; Cheng, F.; Asahara, M.; and Kobayashi, I. 2020 · 2020
Cited alongside, same era.
Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing
Thompson, B.; and Post, M. 2020a · 2020
Cited alongside, same era.
Towards robustness against natural language word substitutions
Dong, X.; Luu, A. T.; Ji, R.; and Liu, H. 2021a
Cited in the paper.
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L.; and Liang, P. 2021 · 2021
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Unsupervised Paraphrasing with Pretrained Language Models
Niu, T.; Yavuz, S.; Zhou, Y.; Keskar, N. S.; Wang, H.; and Xiong, C. 2021 · 2021
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Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning
Chowdhury, J. R.; Zhuang, Y.; and Wang, S. 2022 · 2022
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P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks
Liu, X.; Ji, K.; Fu, Y.; Tam, W.; Du, Z.; Yang, Z.; and Tang, J. 2022 · 2022
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On the Evaluation Metrics for Paraphrase Generation
Shen, L.; Liu, L.; Jiang, H.; and Shi, S. 2022 · 2022
Later among the works it cites.