  As many academic conferences are overwhelmed by a rapidly increasing number
of paper submissions, automatically finding appropriate reviewers for each
submission becomes a more urgent need than ever. Various factors have been
considered by previous attempts on this task to measure the expertise relevance
between a paper and a reviewer, including whether the paper is semantically
close to, shares topics with, and cites previous papers of the reviewer.
However, the majority of previous studies take only one of these factors into
account, leading to an incomprehensive evaluation of paper-reviewer relevance.
To bridge this gap, in this paper, we propose a unified model for
paper-reviewer matching that jointly captures semantic, topic, and citation
factors. In the unified model, a contextualized language model backbone is
shared by all factors to learn common knowledge, while instruction tuning is
introduced to characterize the uniqueness of each factor by producing
factor-aware paper embeddings. Experiments on four datasets (one of which is
newly contributed by us) across different fields, including machine learning,
computer vision, information retrieval, and data mining, consistently validate
the effectiveness of our proposed UniPR model in comparison with
state-of-the-art paper-reviewer matching methods and scientific pre-trained
language models.
