|Title||Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network|
|Affiliation||Peking 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.
|Publisher||PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2|
|Citation||PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2. 2018, 496-502.|
|Abstract||As 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|
|Appears in Collections:||信息科学技术学院|