  To advance the development of science and technology, research proposals are
submitted to open-court competitive programs developed by government agencies
(e.g., NSF). Proposal classification is one of the most important tasks to
achieve effective and fair review assignments. Proposal classification aims to
classify a proposal into a length-variant sequence of labels. In this paper, we
formulate the proposal classification problem into a hierarchical multi-label
classification task. Although there are certain prior studies, proposal
classification exhibit unique features: 1) the classification result of a
proposal is in a hierarchical discipline structure with different levels of
granularity; 2) proposals contain multiple types of documents; 3) domain
experts can empirically provide partial labels that can be leveraged to improve
task performances. In this paper, we focus on developing a new deep proposal
classification framework to jointly model the three features. In particular, to
sequentially generate labels, we leverage previously-generated labels to
predict the label of next level; to integrate partial labels from experts, we
use the embedding of these empirical partial labels to initialize the state of
neural networks. Our model can automatically identify the best length of label
sequence to stop next label prediction. Finally, we present extensive results
to demonstrate that our method can jointly model partial labels, textual
information, and semantic dependencies in label sequences, and, thus, achieve
advanced performances.
