EMNLP 2021main98 citations
Paraphrase Generation: A Survey of the State of the Art
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.
BibTeX
@inproceedings{zhou-bhat-2021-paraphrase,
title = "Paraphrase Generation: A Survey of the State of the Art",
author = "Zhou, Jianing and
Bhat, Suma",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.414/",
doi = "10.18653/v1/2021.emnlp-main.414",
pages = "5075--5086"
}