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Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network

2018-05-10ACL 2018Code Available0· sign in to hype

Pengcheng Yang, Xu sun, Wei Li, Shuming Ma

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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

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