@inproceedings{yu-etal-2021-sentence,
title = "Sentence-Permuted Paragraph Generation",
author = "Yu, Wenhao and
Zhu, Chenguang and
Zhao, Tong and
Guo, Zhichun and
Jiang, Meng",
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.412",
doi = "10.18653/v1/2021.emnlp-main.412",
pages = "5051--5062",
abstract = "Generating paragraphs of diverse contents is important in many applications. Existing generation models produce similar contents from homogenized contexts due to the fixed left-to-right sentence order. Our idea is permuting the sentence orders to improve the content diversity of multi-sentence paragraph. We propose a novel framework PermGen whose objective is to maximize the expected log-likelihood of output paragraph distributions with respect to all possible sentence orders. PermGen uses hierarchical positional embedding and designs new procedures for training, and decoding in the sentence-permuted generation. Experiments on three paragraph generation benchmarks demonstrate PermGen generates more diverse outputs with a higher quality than existing models.",
}
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<abstract>Generating paragraphs of diverse contents is important in many applications. Existing generation models produce similar contents from homogenized contexts due to the fixed left-to-right sentence order. Our idea is permuting the sentence orders to improve the content diversity of multi-sentence paragraph. We propose a novel framework PermGen whose objective is to maximize the expected log-likelihood of output paragraph distributions with respect to all possible sentence orders. PermGen uses hierarchical positional embedding and designs new procedures for training, and decoding in the sentence-permuted generation. Experiments on three paragraph generation benchmarks demonstrate PermGen generates more diverse outputs with a higher quality than existing models.</abstract>
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%0 Conference Proceedings
%T Sentence-Permuted Paragraph Generation
%A Yu, Wenhao
%A Zhu, Chenguang
%A Zhao, Tong
%A Guo, Zhichun
%A Jiang, Meng
%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 yu-etal-2021-sentence
%X Generating paragraphs of diverse contents is important in many applications. Existing generation models produce similar contents from homogenized contexts due to the fixed left-to-right sentence order. Our idea is permuting the sentence orders to improve the content diversity of multi-sentence paragraph. We propose a novel framework PermGen whose objective is to maximize the expected log-likelihood of output paragraph distributions with respect to all possible sentence orders. PermGen uses hierarchical positional embedding and designs new procedures for training, and decoding in the sentence-permuted generation. Experiments on three paragraph generation benchmarks demonstrate PermGen generates more diverse outputs with a higher quality than existing models.
%R 10.18653/v1/2021.emnlp-main.412
%U https://aclanthology.org/2021.emnlp-main.412
%U https://doi.org/10.18653/v1/2021.emnlp-main.412
%P 5051-5062
Markdown (Informal)
[Sentence-Permuted Paragraph Generation](https://aclanthology.org/2021.emnlp-main.412) (Yu et al., EMNLP 2021)
ACL
- Wenhao Yu, Chenguang Zhu, Tong Zhao, Zhichun Guo, and Meng Jiang. 2021. Sentence-Permuted Paragraph Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5051–5062, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.