Ranking Scientific Papers Using Preference Learning
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Peer review is the main quality control mechanism in academia. Quality of scientific work has many dimensions; coupled with the subjective nature of the reviewing task, this makes final decision making based on the reviews and scores therein very difficult and time-consuming. To assist with this important task, we cast it as a paper ranking problem based on peer review texts and reviewer scores. We introduce a novel, multi-faceted generic evaluation framework for making final decisions based on peer reviews that takes into account effectiveness, efficiency and fairness of the evaluated system. We propose a novel approach to paper ranking based on Gaussian Process Preference Learning (GPPL) and evaluate it on peer review data from the ACL-2018 conference. Our experiments demonstrate the superiority of our GPPL-based approach over prior work, while highlighting the importance of using both texts and review scores for paper ranking during peer review aggregation.
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