Paraphrase Generation: A Survey of the State of the Art
Jianing Zhou, Suma Bhat
2021Year
2Citations
13Top-tier citations
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, diverse and human-like paraphrases. This paper surveys various approaches to paraphrase generation with a main focus on neural methods.
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Install the CLIlune papers fulltext 1cd70058-42ef-49fb-ad5c-79010046af4dCited by top-tier papers13
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