Text Generation with Text-Editing Models. (arXiv:2206.07043v1 [cs.CL])

Text-editing models have recently become a prominent alternative to seq2seq
models for monolingual text-generation tasks such as grammatical error
correction, simplification, and style transfer. These tasks share a common
trait - they exhibit a large amount of textual overlap between the source and
target texts. Text-editing models take advantage of this observation and learn
to generate the output by predicting edit operations applied to the source
sequence. In contrast, seq2seq models generate outputs word-by-word from
scratch thus making them slow …

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