Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network

Pengcheng Yang, Xu Sun, Wei Li, Shuming Ma


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
Anthology ID:
P18-2079
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
496–502
Language:
URL:
https://aclanthology.org/P18-2079
DOI:
10.18653/v1/P18-2079
Bibkey:
Cite (ACL):
Pengcheng Yang, Xu Sun, Wei Li, and Shuming Ma. 2018. Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 496–502, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network (Yang et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-2079.pdf
Poster:
 P18-2079.Poster.pdf
Code
 lancopku/AAPR
Data
Arxiv Academic Paper Dataset