Paraphrase Generation: A Survey of the State of the Art
Jianing Zhou, Suma Bhat
2021年份
2被引次数
13顶会引用
摘要
This paper focuses on paraphrase generation, which is a widely studied natural language generation task in NLP. With the development of neural models, paraphrase generation research has exhibited a gradual shift to neural methods in the recent years. This has provided architectures for contextualized representation of an input text and generating fluent, diverse and human-like paraphrases. This paper surveys various approaches to paraphrase generation with a main focus on neural methods.
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引用它的顶会 Paper13
- Don't Trust ChatGPT when your Question is not in English: A Study of Multilingual Abilities and Types of LLMsXiang Zhang, Senyu Li, Bradley Hauer, Ning Shi 等EMNLP 2023 · 被引用 58 次
- Wisdom of Two Crowds: Misinformation Moderation on Reddit and How to Improve this Process - A Case Study of COVID-19Lia Bozarth, Jane Im, Christopher Chad Quarles, Ceren BudakCSCW 2023 · 被引用 31 次
- How Large Language Models are Transforming Machine-Paraphrase PlagiarismJan Philip Wahle, Terry Ruas, Frederic Kirstein, Bela GippEMNLP 2022 · 被引用 26 次
- Principled Paraphrase Generation with Parallel CorporaAitor Ormazabal, Mikel Artetxe, Aitor Soroa, Gorka Labaka 等ACL 2022 · 被引用 12 次
- Improving Large-scale Paraphrase Acquisition and GenerationYao Dou, Chao Jiang, Wei XuEMNLP 2022 · 被引用 11 次
它引用的顶会 Paper7
- Paraphrase Augmented Task-Oriented Dialog GenerationSilin Gao, Yichi Zhang, Zhijian Ou, Zhou YuACL 2020 · 被引用 78 次
- Paraphrase Generation by Learning How to Edit from SamplesAmirhossein Kazemnejad, Mohammadreza Salehi, Mahdieh Soleymani BaghshahACL 2020 · 被引用 43 次
- Unsupervised Dual Paraphrasing for Two-stage Semantic ParsingRuisheng Cao, Su Zhu, Chenyu Yang, Chen Liu 等ACL 2020 · 被引用 37 次
- Unsupervised Paraphrasing via Deep Reinforcement LearningA. B. Siddique, Samet Oymak, Vagelis HristidisKDD 2020 · 被引用 27 次
- Neural Syntactic Preordering for Controlled Paraphrase GenerationTanya Goyal, Greg DurrettACL 2020 · 被引用 9 次
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