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        <title>Enhancing Topic-to-Essay Generation with External Commonsense Knowledge</title>
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    <abstract>Automatic topic-to-essay generation is a challenging task since it requires generating novel, diverse, and topic-consistent paragraph-level text with a set of topics as input. Previous work tends to perform essay generation based solely on the given topics while ignoring massive commonsense knowledge. However, this commonsense knowledge provides additional background information, which can help to generate essays that are more novel and diverse. Towards filling this gap, we propose to integrate commonsense from the external knowledge base into the generator through dynamic memory mechanism. Besides, the adversarial training based on a multi-label discriminator is employed to further improve topic-consistency. We also develop a series of automatic evaluation metrics to comprehensively assess the quality of the generated essay. Experiments show that with external commonsense knowledge and adversarial training, the generated essays are more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation.</abstract>
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%0 Conference Proceedings
%T Enhancing Topic-to-Essay Generation with External Commonsense Knowledge
%A Yang, Pengcheng
%A Li, Lei
%A Luo, Fuli
%A Liu, Tianyu
%A Sun, Xu
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F yang-etal-2019-enhancing-topic
%X Automatic topic-to-essay generation is a challenging task since it requires generating novel, diverse, and topic-consistent paragraph-level text with a set of topics as input. Previous work tends to perform essay generation based solely on the given topics while ignoring massive commonsense knowledge. However, this commonsense knowledge provides additional background information, which can help to generate essays that are more novel and diverse. Towards filling this gap, we propose to integrate commonsense from the external knowledge base into the generator through dynamic memory mechanism. Besides, the adversarial training based on a multi-label discriminator is employed to further improve topic-consistency. We also develop a series of automatic evaluation metrics to comprehensively assess the quality of the generated essay. Experiments show that with external commonsense knowledge and adversarial training, the generated essays are more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation.
%R 10.18653/v1/P19-1193
%U https://aclanthology.org/P19-1193
%U https://doi.org/10.18653/v1/P19-1193
%P 2002-2012

Markdown (Informal)

[Enhancing Topic-to-Essay Generation with External Commonsense Knowledge](https://aclanthology.org/P19-1193) (Yang et al., ACL 2019)

• Enhancing Topic-to-Essay Generation with External Commonsense Knowledge (Yang et al., ACL 2019)

ACL

• Pengcheng Yang, Lei Li, Fuli Luo, Tianyu Liu, and Xu Sun. 2019. Enhancing Topic-to-Essay Generation with External Commonsense Knowledge. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2002–2012, Florence, Italy. Association for Computational Linguistics.