@inproceedings{faltings-etal-2021-text,
title = "Text Editing by Command",
author = "Faltings, Felix and
Galley, Michel and
Hintz, Gerold and
Brockett, Chris and
Quirk, Chris and
Gao, Jianfeng and
Dolan, Bill",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.414",
doi = "10.18653/v1/2021.naacl-main.414",
pages = "5259--5274",
abstract = "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.",
}
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%0 Conference Proceedings
%T Text Editing by Command
%A Faltings, Felix
%A Galley, Michel
%A Hintz, Gerold
%A Brockett, Chris
%A Quirk, Chris
%A Gao, Jianfeng
%A Dolan, Bill
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F faltings-etal-2021-text
%X 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.
%R 10.18653/v1/2021.naacl-main.414
%U https://aclanthology.org/2021.naacl-main.414
%U https://doi.org/10.18653/v1/2021.naacl-main.414
%P 5259-5274
Markdown (Informal)
[Text Editing by Command](https://aclanthology.org/2021.naacl-main.414) (Faltings et al., NAACL 2021)
ACL
- Felix Faltings, Michel Galley, Gerold Hintz, Chris Brockett, Chris Quirk, Jianfeng Gao, and Bill Dolan. 2021. Text Editing by Command. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5259–5274, Online. Association for Computational Linguistics.