@inproceedings{spangher-etal-2022-sequentially,
title = "Sequentially Controlled Text Generation",
author = "Spangher, Alexander and
Ming, Yao and
Hua, Xinyu and
Peng, Nanyun",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.509",
doi = "10.18653/v1/2022.findings-emnlp.509",
pages = "6848--6866",
abstract = "While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We propose a novel controlled text generation task, sequentially controlled text generation, and identify a dataset, NewsDiscourse as a starting point for this task. We develop a sequential controlled text generation pipeline with generation and editing. We test different degrees of structural awareness and show that, in general, more structural awareness results in higher control- accuracy, grammaticality, coherency and topicality, approaching human-level writing performance.",
}
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<abstract>While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We propose a novel controlled text generation task, sequentially controlled text generation, and identify a dataset, NewsDiscourse as a starting point for this task. We develop a sequential controlled text generation pipeline with generation and editing. We test different degrees of structural awareness and show that, in general, more structural awareness results in higher control- accuracy, grammaticality, coherency and topicality, approaching human-level writing performance.</abstract>
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%0 Conference Proceedings
%T Sequentially Controlled Text Generation
%A Spangher, Alexander
%A Ming, Yao
%A Hua, Xinyu
%A Peng, Nanyun
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F spangher-etal-2022-sequentially
%X While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We propose a novel controlled text generation task, sequentially controlled text generation, and identify a dataset, NewsDiscourse as a starting point for this task. We develop a sequential controlled text generation pipeline with generation and editing. We test different degrees of structural awareness and show that, in general, more structural awareness results in higher control- accuracy, grammaticality, coherency and topicality, approaching human-level writing performance.
%R 10.18653/v1/2022.findings-emnlp.509
%U https://aclanthology.org/2022.findings-emnlp.509
%U https://doi.org/10.18653/v1/2022.findings-emnlp.509
%P 6848-6866
Markdown (Informal)
[Sequentially Controlled Text Generation](https://aclanthology.org/2022.findings-emnlp.509) (Spangher et al., Findings 2022)
ACL
- Alexander Spangher, Yao Ming, Xinyu Hua, and Nanyun Peng. 2022. Sequentially Controlled Text Generation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6848–6866, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.