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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/P18-2079.pdf
- Code
- lancopku/AAPR
- Data
- Arxiv Academic Paper Dataset