LAUSR

Creating the schedule for an academic conference is a time-consuming task. A typical conference schedule consists of sessions containing papers addressing the same research topic. To construct a schedule, conference papers must be grouped according to their research topic, and the obtained groups should fit the assigned time slots. This paper proposes an approach to automating the schedule-creation process. We use multilingual, neuro-symbolic paper representations and novel constrained clustering to group papers into clusters of predetermined size with the same topic fitting the schedule structure. In the process, we combine machine-learning, natural language processing, network analysis, and combinatorial optimization. We tested the components of the proposed approach on a newly created database of papers from six machine learning conferences, which were manually labeled by their research topics. The entire system was tested on two real-world conferences in a multilingual setting. The developed methodology is incorporated into an interactive automatic conference-scheduling system NeSyChair (Neuro-Symbolic Conference Chair), which can be used to create and improve conference schedules.