@InProceedings{yang-EtAl:2018:Short1,
  author    = {Yang, Pengcheng  and  SUN, Xu  and  Li, Wei  and  Ma, Shuming},
  title     = {Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {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 \url{https://github.com/lancopku/AAPR}},
  url       = {http://www.aclweb.org/anthology/P18-2079}
}