{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatic-academic-paper-rating-based-on","title":"Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network","arxiv_id":"1805.03977","date":"2018-05-10","proceeding":"ACL 2018 7","authors":["Pengcheng Yang","Xu sun","Wei Li","Shuming Ma"],"abstract":"As more and more academic papers are being submitted to conferences and\njournals, evaluating all these papers by professionals is time-consuming and\ncan cause inequality due to the personal factors of the reviewers. In this\npaper, in order to assist professionals in evaluating academic papers, we\npropose a novel task: automatic academic paper rating (AAPR), which\nautomatically determine whether to accept academic papers. We build a new\ndataset for this task and propose a novel modularized hierarchical\nconvolutional neural network to achieve automatic academic paper rating.\nEvaluation results show that the proposed model outperforms the baselines by a\nlarge margin. The dataset and code are available at\n\\url{https://github.com/lancopku/AAPR}","url_abs":"http://arxiv.org/abs/1805.03977v1","url_pdf":"http://arxiv.org/pdf/1805.03977v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automatic-academic-paper-rating-based-on","repo_url":"https://github.com/lancopku/AAPR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"arxiv-academic-paper-dataset","name":"Arxiv Academic Paper Dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}