@inproceedings{yang-etal-2018-automatic,
title = "Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network",
author = "Yang, Pengcheng and
Sun, Xu and
Li, Wei and
Ma, Shuming",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2079",
doi = "10.18653/v1/P18-2079",
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}",
}
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<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</abstract>
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%0 Conference Proceedings
%T Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network
%A Yang, Pengcheng
%A Sun, Xu
%A Li, Wei
%A Ma, Shuming
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F yang-etal-2018-automatic
%X 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
%R 10.18653/v1/P18-2079
%U https://aclanthology.org/P18-2079
%U https://doi.org/10.18653/v1/P18-2079
%P 496-502
Markdown (Informal)
[Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network](https://aclanthology.org/P18-2079) (Yang et al., ACL 2018)
ACL