  While large-scale pretrained language models have significantly improved
writing assistance functionalities such as autocomplete, more complex and
controllable writing assistants have yet to be explored. We leverage advances
in language modeling to build an interactive writing assistant that generates
and rephrases text according to fine-grained author specifications. Users
provide input to our Intent-Guided Assistant (IGA) in the form of text
interspersed with tags that correspond to specific rhetorical directives (e.g.,
adding description or contrast, or rephrasing a particular sentence). We
fine-tune a language model on a dataset heuristically-labeled with author
intent, which allows IGA to fill in these tags with generated text that users
can subsequently edit to their liking. A series of automatic and crowdsourced
evaluations confirm the quality of IGA's generated outputs, while a small-scale
user study demonstrates author preference for IGA over baseline methods in a
creative writing task. We release our dataset, code, and demo to spur further
research into AI-assisted writing.
