  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.
