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.

翻译：科研项目的同行评议是决定资助授予的主要机制。然而，科研项目日益呈现跨学科特征。如何将跨学科项目分派给合适的评审专家以进行公正评估，一直是长期存在的挑战。评审专家分配的关键步骤之一，是为项目-评审匹配生成准确的跨学科主题标签。现有系统主要收集项目负责人手动生成的学科主题标签。但此类人工标注标签存在不准确、不完整、耗时耗力等缺陷。人工智能如何在开发公平精准的评审分配系统中发挥作用？本研究与国家自然科学基金委员会合作，致力于解决自动跨学科主题路径检测任务。为此，我们开发了深度层级化跨学科科研项目分类网络（HIRPCN）。具体而言，我们首先提出层级化Transformer来提取项目的文本语义信息；继而设计跨学科图结构，并利用图神经网络学习各学科表示以提取跨学科知识；在完成语义与跨学科知识提取后，创新性地设计逐层预测组件来融合两类知识表征，为每个项目检测跨学科主题路径。我们在三个真实数据集上开展大量实验与专家评估，验证了所提模型的有效性。