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.
@article{arxiv.2104.07000,
title = {IGA : An Intent-Guided Authoring Assistant},
author = {Simeng Sun and Wenlong Zhao and Varun Manjunatha and Rajiv Jain and Vlad Morariu and Franck Dernoncourt and Balaji Vasan Srinivasan and Mohit Iyyer},
journal= {arXiv preprint arXiv:2104.07000},
year = {2021}
}