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Peer assessment has been widely applied across diverse academic fields overthe last few decades and has demonstrated its effectiveness. However, theadvantages of peer assessment can only be achieved with high-quality peerreviews. Previous studies have found that high-quality review comments usuallycomprise several features (e.g., contain suggestions, mention problems, use apositive tone). Thus, researchers have attempted to evaluate peer-reviewcomments by detecting different features using various machine learning anddeep learning models. However, there is no single study that investigates usinga multi-task learning (MTL) model to detect multiple features simultaneously.This paper presents two MTL models for evaluating peer-review comments byleveraging the state-of-the-art pre-trained language representation models BERTand DistilBERT. Our results demonstrate that BERT-based models significantlyoutperform previous GloVe-based methods by around 6% in F1-score on tasks ofdetecting a single feature, and MTL further improves performance while reducingmodel size.

ALL-IN-ONE: Multi-Task Learning BERT models for Evaluating Peer Assessments