TitleEnhancing Topic-to-Essay Generation with External Commonsense Knowledge
AuthorsYang, Pengcheng
Li, Lei
Luo, Fuli
Liu, Tianyu
Sun, Xu
AffiliationPeking Univ, Beijing Inst Big Data Res, Deep Learning Lab, Beijing, Peoples R China
Peking Univ, Sch EECS, MOE Key Lab Computat Linguist, Beijing, Peoples R China
Xidian Univ, Sch Comp Sci & Technol, Xian, Shaanxi, Peoples R China
Issue Date2019
Publisher57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019)
AbstractAutomatic 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.
URIhttp://hdl.handle.net/20.500.11897/552787
IndexedISSHP
CPCI-S(ISTP)
Appears in Collections:信息科学技术学院
计算语言学教育部重点实验室

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