Labels are not necessary: Assessing peer-review helpfulness using domain adaptation based on self-training (2023)

Abstract: A peer-assessment system allows students to provide feedback on each other’s work. An effective peer assessment system urgently requires helpful reviews to facilitate students to make improvements and progress. Automated evaluation of review helpfulness, with the help of deep learning models and natural language processing techniques, gains much interest in the field of peer assessment. However, collecting labeled data with the “helpfulness” tag to build these prediction models remains challenging. A straightforward solution would be using a supervised learning algorithm to train a prediction model on a similar domain and apply it to our peer review domain for inference. But naively doing so can degrade the model performance in the presence of the distributional gap between domains. Such a distributional gap can be effectively addressed by Domain Adaptation (DA). Self-training has recently been shown as a powerful branch of DA to address the distributional gap. The first goal of this study is to evaluate the performance of self-training-based DA in predicting the helpfulness of peer reviews as well as the ability to overcome the distributional gap. Our second goal is to propose an advanced self-training framework to overcome the weakness of the existing self-training by tailoring knowledge distillation and noise injection, to further improve the model performance and better address the distributional gap.

## introduction peer review is a learning tool that enables students to evaluate their peers' assignments or projects @xcite @xcite . it can help instructors enhance their teaching @xcite , and allow students to develop skills in assessing and providing feedback to others. figure 1 illustrates the steps of the peer review process. it starts with the authors submitting their work. the peers then evaluate the work and provide both textual feedback and numerical scores. the author assesses the feedback and tends to accept only the helpful reviews to make further revisions @xcite . the instructors can refer the numerical scores provided by the reviewers to give the final grades. therefore, identifying helpful peer reviews can enhance the benefits to students from the peer-review process @xcite . automatic recognition of peer-review helpfulness has been studied limitedly with the help of deep learning models and natural language processing @xcite . however, in order to create a reliable model that can accurately predict helpfulness, a considerable amount of peer-review data labeled with helpfulness is required @xcite . the students receiving the reviews are the most suitable individuals to label the data, but the difficulty in collecting labeled reviews from students poses a challenge. moreover, the subjective nature of "helpfulness" creates ambiguity, making it challenging to achieve a consensus in a team on whether a review is helpful. as a result, obtaining sufficient labeled data to develop a robust model for predicting helpfulness remains a significant obstacle. a straightforward solution to overcome the challenges of collecting labeled data is to adapt a model trained on a pre-existing labeled dataset from a similar domain that includes "helpfulness" tags to our peer review domain. specifically, we can train the prediction model on a "source domain" labeled data following the supervised manner, and generate "helpfulness" prediction on the "target domain" unlabeled data from our peer reviews. however, the discrepancies in data distribution between the source and target domains, i.e., domain shift @xcite , can cause the model's performance to degrade on unseen target domain data. in this paper, in order to address the domain shift issue, we propose to apply self-training (a.k.a., pseudo-labeling) @xcite @xcite @xcite , as a promising technique in domain adaptation (da) ( @xcite @xcite ). self-training-based domain adaptation aims to transfer knowledge learned from the source domain to the target domain, by involving the unlabeled data from the target domain in the model training. we hypothesize that learning from the unlabeled data can enhance the generalization ability, and facilitate the effective knowledge transfer across domains. this hypothesis will be validated through the experiment results. our proposed approach for domain adaptation using self-training follows the "student-teacher model" framework @xcite . as shown in figure 3 , the student and teacher models will constantly exchange their roles during the iterative process, and the student model will continuously learn from the pseudo labels predicted by the teacher model. self-training helps to overcome the domain shift between the source and target domains @xcite . as a novelty, our study also proposes an advanced self-training framework that utilizes knowledge distillation @xcite and noise injection @xcite techniques to overcome some weaknesses of the traditional self-training, and further improve the adaptation performance. by incorporating knowledge distillation, the student model can better mimic the teacher model and break through the limitation of only being able to learn from the "hard labels" provided by the teacher model. additionally, the incorporation of noise injection enables the student model to outperform the teacher model by learning from the augmented data, which is beyond what the teacher model predicts. the contributions are summarized as follows: however, there is no study that directly investigates predicting helpfulness based on the semantics of the review content. the lack of labeled training data also poses a challenge to building such a prediction model, due to the subjective nature of helpfulness and controversies surrounding its definition. @xcite reports that there is a great deal of variation among students and even domain experts in terms of "what constitutes a helpful comment." fortunately, several researchers @xcite @xcite have explored predicting the helpfulness of online product reviews, which can be conveniently labeled with "helpfulness" through user voting from online shopping platforms. in this study, we adapt the task of predicting the helpfulness of online product reviews to our academic peer reviews, drastically reducing the need for collecting peer-review labeled data. ## conclusion this study first highlights the pedagogical significance of predicting helpful reviews in peer assessment to benefit student learning, and then considers the challenge of collecting labeled data to build a reliable prediction model. we explore a solution via domain adaptation to reduce the need of collecting labeled data. our primary contribution is proposing self-training as an optimal domain-adaptation technique to address the domain-shift issue that commonly arises when transferring knowledge between domains. furthermore, we incorporate knowledge distillation and noise injection into self-training to improve performance. the experimental results exhibit promise in utilizing self-training and show the effectiveness of our proposed approach. in addition, we discuss future work in optimizing the integration of knowledge distillation and noise injection.