Papers › Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network
Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network
Pengcheng Yang, Xu sun, Wei Li, Shuming Ma
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 \url{https://github.com/lancopku/AAPR}
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