Paraphrase Generation: A Survey of the State of the Art

Jianing Zhou, Suma Bhat


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.
Anthology ID:
2021.emnlp-main.414
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5075–5086
Language:
URL:
https://aclanthology.org/2021.emnlp-main.414
DOI:
10.18653/v1/2021.emnlp-main.414
Bibkey:
Cite (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.
Cite (Informal):
Paraphrase Generation: A Survey of the State of the Art (Zhou & Bhat, EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.414.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.414.mp4
Data
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