  A prevailing paradigm in neural text generation is one-shot generation, where
text is produced in a single step. The one-shot setting is inadequate, however,
when the constraints the user wishes to impose on the generated text are
dynamic, especially when authoring longer documents. We address this limitation
with an interactive text generation setting in which the user interacts with
the system by issuing commands to edit existing text. To this end, we propose a
novel text editing task, and introduce WikiDocEdits, a dataset of
single-sentence edits crawled from Wikipedia. We show that our Interactive
Editor, a transformer-based model trained on this dataset, outperforms
baselines and obtains positive results in both automatic and human evaluations.
We present empirical and qualitative analyses of this model's performance.
