  Neural sequence-to-sequence models are finding increasing use in editing of
documents, for example in correcting a text document or repairing source code.
In this paper, we argue that common seq2seq models (with a facility to copy
single tokens) are not a natural fit for such tasks, as they have to explicitly
copy each unchanged token. We present an extension of seq2seq models capable of
copying entire spans of the input to the output in one step, greatly reducing
the number of decisions required during inference. This extension means that
there are now many ways of generating the same output, which we handle by
deriving a new objective for training and a variation of beam search for
inference that explicitly handles this problem. In our experiments on a range
of editing tasks of natural language and source code, we show that our new
model consistently outperforms simpler baselines.
