@inproceedings{sun-etal-2021-iga,
title = "{IGA}: An Intent-Guided Authoring Assistant",
author = "Sun, Simeng and
Zhao, Wenlong and
Manjunatha, Varun and
Jain, Rajiv and
Morariu, Vlad and
Dernoncourt, Franck and
Srinivasan, Balaji Vasan and
Iyyer, Mohit",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.483",
doi = "10.18653/v1/2021.emnlp-main.483",
pages = "5972--5985",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T IGA: An Intent-Guided Authoring Assistant
%A Sun, Simeng
%A Zhao, Wenlong
%A Manjunatha, Varun
%A Jain, Rajiv
%A Morariu, Vlad
%A Dernoncourt, Franck
%A Srinivasan, Balaji Vasan
%A Iyyer, Mohit
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F sun-etal-2021-iga
%X 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.
%R 10.18653/v1/2021.emnlp-main.483
%U https://aclanthology.org/2021.emnlp-main.483
%U https://doi.org/10.18653/v1/2021.emnlp-main.483
%P 5972-5985
Markdown (Informal)
[IGA: An Intent-Guided Authoring Assistant](https://aclanthology.org/2021.emnlp-main.483) (Sun et al., EMNLP 2021)
ACL
- Simeng Sun, Wenlong Zhao, Varun Manjunatha, Rajiv Jain, Vlad Morariu, Franck Dernoncourt, Balaji Vasan Srinivasan, and Mohit Iyyer. 2021. IGA: An Intent-Guided Authoring Assistant. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5972–5985, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.