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Syntactically controlled paraphrase generation requires language models to generate paraphrases for sentences according to specific syntactic structures.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Simple fast algorithms for the editing distance between trees and related problems
Kaizhong Zhang and Dennis Shasha. 1989 · 1989
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources
Bill Dolan, Chris Quirk, and Chris Brockett. 2004 · 2004
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Conditioned natural language generation using only unconditioned language model: An exploration
Fan-Keng Sun and Cheng-I Lai. 2020 · 2011
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Re-examining machine translation metrics for paraphrase identification
Nitin Madnani, Joel Tetreault, and Martin Chodorow. 2012 · 2012
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First quora dataset release: Question pairs
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Decoupled weight decay regularization
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Controllable abstractive summarization
Angela Fan, David Grangier, and Michael Auli. 2018 · 2018
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Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
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Polite dialogue generation without parallel data
Tong Niu and Mohit Bansal. 2018 · 2018
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A multi-task approach for disentangling syntax and semantics in sentence representations
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Paraphrase augmented task-oriented dialog generation
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The power of scale for parameter-efficient prompt tuning
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Noisy channel language model prompting for few-shot text classification
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Aesop: Paraphrase generation with adaptive syntactic control
Jiao Sun, Xuezhe Ma, and Nanyun Peng. 2021 · 2021
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HypoGen: Hyperbole generation with commonsense and counterfactual knowledge
Yufei Tian, Arvind krishna Sridhar, and Nanyun Peng. 2021 · 2021
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Syntax-guided controlled generation of paraphrases
Ashutosh Kumar, Kabir Ahuja, Raghuram Vadapalli, and Partha Talukdar. 2020 · 2020
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Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Generating syntactically controlled paraphrases without using annotated parallel pairs
Kuan-Hao Huang and Kai-Wei Chang. 2021 · 2021
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P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2021a
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Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021b
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Unsupervised syntactically controlled paraphrase generation with abstract meaning representations
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SPoT: Better frozen model adaptation through soft prompt transfer
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