Reviewer assignment based on sentence pair modeling

Abstract Assigning appropriate reviewers to a manuscript from a pool of candidate reviewers is an important task in the academic community. Recent researches have focused mainly on text processing methods based on natural language processing, such as topic model, word embedding and so on. However, it is difficult for the computer to understand the research fields of reviewers and manuscripts. In this paper, a novel supervisory information that is expressed as sentence pairs constructed by titles and abstracts is adopted to solve reviewer assignment problem. We propose a sentence pair modeling-based reviewer assignment (SPM-RA) method, which models the relationship of sentence pairs by supervising information. The supervisory information makes the model accurately learn the field features of reviewers and manuscripts. Firstly, we construct the training set by the field relationship between title and abstract. We use TF-IDF sampling to solve the problem of unbalanced data set. Then, we use neural network models, such as the BERT (bidirectional encoder representations from transformers), CNN (convolution neural network), biLSTM (bidirectional long short term memory), or various combinations of them to do modeling sentence pair and learn the field features of the paper through the training set. Finally, we predict the similarity between reviewers and manuscripts by training model. We evaluate SPM-RA on two real datasets and compare its performance to that of seven existing methods. The experimental results show that SPM-RA improves the recommendation precision by at least 18% on public dataset.