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    <abstract>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, diverseand human-like paraphrases. This paper surveys various approaches to paraphrase generation with a main focus on neural methods.</abstract>
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%0 Conference Proceedings
%T Paraphrase Generation: A Survey of the State of the Art
%A Zhou, Jianing
%A Bhat, Suma
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F zhou-bhat-2021-paraphrase
%X 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, diverseand human-like paraphrases. This paper surveys various approaches to paraphrase generation with a main focus on neural methods.
%R 10.18653/v1/2021.emnlp-main.414
%U https://aclanthology.org/2021.emnlp-main.414
%U https://doi.org/10.18653/v1/2021.emnlp-main.414
%P 5075-5086

Markdown (Informal)

[Paraphrase Generation: A Survey of the State of the Art](https://aclanthology.org/2021.emnlp-main.414) (Zhou & Bhat, EMNLP 2021)

• Paraphrase Generation: A Survey of the State of the Art (Zhou & Bhat, EMNLP 2021)

ACL

• Jianing Zhou and Suma Bhat. 2021. Paraphrase Generation: A Survey of the State of the Art. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5075–5086, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.