TitleAutomatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network
AuthorsYang, Pengcheng
Sun, Xu
Li, Wei
Ma, Shuming
AffiliationPeking Univ, Sch EECS, Key Lab Computat Linguist, MOE, Beijing, Peoples R China.
Peking Univ, Beijing Inst Big Data Res, Deep Learning Lab, Beijing, Peoples R China.
Issue Date2018
PublisherPROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2
CitationPROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2. 2018, 496-502.
AbstractAs more and more academic papers are being submitted to conferences and journals, evaluating all these papers by professionals is time-consuming and can cause inequality due to the personal factors of the reviewers. In this paper, in order to assist professionals in evaluating academic papers, we propose a novel task: automatic academic paper rating (AAPR), which automatically determine whether to accept academic papers. We build a new dataset for this task and propose a novel modularized hierarchical convolutional neural network to achieve automatic academic paper rating. Evaluation results show that the proposed model outperforms the baselines by a large margin. The dataset and code are available at https://github.com/lancopku/AAPR
URIhttp://hdl.handle.net/20.500.11897/575064
IndexedCPCI-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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