@inproceedings{xu-etal-2018-skeleton,
title = "A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation",
author = "Xu, Jingjing and
Ren, Xuancheng and
Zhang, Yi and
Zeng, Qi and
Cai, Xiaoyan and
Sun, Xu",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1462",
doi = "10.18653/v1/D18-1462",
pages = "4306--4315",
abstract = "Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing generative models. To address this problem, we propose a skeleton-based model to promote the coherence of generated stories. Different from traditional models that generate a complete sentence at a stroke, the proposed model first generates the most critical phrases, called skeleton, and then expands the skeleton to a complete and fluent sentence. The skeleton is not manually defined, but learned by a reinforcement learning method. Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation. The G-score is improved by 20.1{\%} in human evaluation.",
}
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<abstract>Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing generative models. To address this problem, we propose a skeleton-based model to promote the coherence of generated stories. Different from traditional models that generate a complete sentence at a stroke, the proposed model first generates the most critical phrases, called skeleton, and then expands the skeleton to a complete and fluent sentence. The skeleton is not manually defined, but learned by a reinforcement learning method. Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation. The G-score is improved by 20.1% in human evaluation.</abstract>
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%0 Conference Proceedings
%T A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation
%A Xu, Jingjing
%A Ren, Xuancheng
%A Zhang, Yi
%A Zeng, Qi
%A Cai, Xiaoyan
%A Sun, Xu
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F xu-etal-2018-skeleton
%X Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing generative models. To address this problem, we propose a skeleton-based model to promote the coherence of generated stories. Different from traditional models that generate a complete sentence at a stroke, the proposed model first generates the most critical phrases, called skeleton, and then expands the skeleton to a complete and fluent sentence. The skeleton is not manually defined, but learned by a reinforcement learning method. Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation. The G-score is improved by 20.1% in human evaluation.
%R 10.18653/v1/D18-1462
%U https://aclanthology.org/D18-1462
%U https://doi.org/10.18653/v1/D18-1462
%P 4306-4315
Markdown (Informal)
[A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation](https://aclanthology.org/D18-1462) (Xu et al., EMNLP 2018)
ACL