Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals

• URL: http://arxiv.org/abs/2203.10922v1
• Date: Mon, 7 Mar 2022 03:30:50 GMT
• Title: Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals
• Authors: Meng Xiao, Ziyue Qiao, Yanjie Fu, Hao Dong, Yi Du, Pengyang Wang, Dong Li, Yuanchun Zhou
• Abstract summary: It has been a longstanding challenge to assign proposals to appropriate reviewers. Existing systems mainly collect topic labels manually reported by discipline investigators. What role can AI play in developing a fair and precise proposal review system?
• Score: 24.995369698179317
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The peer merit review of research proposals has been the major mechanism to decide grant awards. Nowadays, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign proposals to appropriate reviewers. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposals. Existing systems mainly collect topic labels manually reported by discipline investigators. However, such human-reported labels can be non-accurate and incomplete. What role can AI play in developing a fair and precise proposal review system? In this evidential study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). We first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs to learn representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.

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