  The peer merit review of research proposals has been the major mechanism for
deciding grant awards. However, research proposals have become increasingly
interdisciplinary. It has been a longstanding challenge to assign
interdisciplinary proposals to appropriate reviewers, so proposals are fairly
evaluated. One of the critical steps in reviewer assignment is to generate
accurate interdisciplinary topic labels for proposal-reviewer matching.
Existing systems mainly collect topic labels manually generated by principal
investigators. However, such human-reported labels can be non-accurate,
incomplete, labor intensive, and time costly. What role can AI play in
developing a fair and precise proposal reviewer assignment system? In this
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). Specifically, we first propose a hierarchical
transformer to extract the textual semantic information of proposals. We then
design an interdisciplinary graph and leverage GNNs for learning
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.
