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.
No takes yet. Share an insight, caveat, or question.
Zhou et al. (2021) studied this question.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: