@inproceedings{1b184e9e433f43e38b96884e4c111157,
title = "Automatic Academic Paper Rating Based on Modularized Hierarchical Attention Network",
abstract = "Automatic academic paper rating (AAPR) remains a difficult but useful task to automatically predict whether to accept or reject a paper. Having found more task-specific structure features of academic papers, we present a modularized hierarchical attention network (MHAN) to predict paper quality. MHAN uses a three-level hierarchical attention network to shorten the sequence for each level. In the network, the modularized parameter distinguishes the semantics of functional chapters. And a label-smoothing mechanism is used as a loss function to avoid inappropriate labeling. Compared with MHCNN and plain HAN on an AAPR dataset, MHAN achieves a state-of-the-art accuracy of 65.33%. Ablation experiments show that the proposed methods are effective.",
keywords = "Automatic academic paper rating, Hierarchical, Modularized",
author = "Kai Kang and Huaping Zhang and Yugang Li and Xi Luo and Silamu Wushour",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 11th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2022 ; Conference date: 24-09-2022 Through 25-09-2022",
year = "2022",
doi = "10.1007/978-3-031-17120-8_52",
language = "English",
isbn = "9783031171192",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "669--681",
editor = "Wei Lu and Shujian Huang and Yu Hong and Xiabing Zhou",
booktitle = "Natural Language Processing and Chinese Computing - 11th CCF International Conference, NLPCC 2022, Proceedings",
address = "Germany",
}