  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}
