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We present EdiT5 - a novel semi-autoregressive text-editing model designed tocombine the strengths of non-autoregressive text-editing and autoregressivedecoding. EdiT5 is faster during inference than conventionalsequence-to-sequence (seq2seq) models, while being capable of modellingflexible input-output transformations.  This is achieved by decomposing the generation process into three sub-tasks:(1) tagging to decide on the subset of input tokens to be preserved in theoutput, (2) re-ordering to define their order in the output text, and (3)insertion to infill the missing tokens that are not present in the input. Thetagging and re-ordering steps, which are responsible for generating the largestportion of the output, are non-autoregressive, while the insertion step uses anautoregressive decoder.  Depending on the task, EdiT5 on average requires significantly fewerautoregressive steps, demonstrating speedups of up to 25x when compared toseq2seq models. Quality-wise, EdiT5 is initialized with a pre-trained T5checkpoint yielding comparable performance to T5 in high-resource settings whenevaluated on three NLG tasks: Sentence Fusion, Grammatical Error Correction,and Decontextualization while clearly outperforming T5 in low-resourcesettings.

EdiT5: Semi-Autoregressive Text-Editing with T5 Warm-Start