Enhancing Automated Essay Scoring Performance Via Fine-Tuning Pre-Trained Language Models with Combination of Regression and Ranking
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking Ruosong Yang†‡, Jiannong Cao†, Zhiyuan Wen†, Youzheng Wu‡, Xiaodong He‡ †Department of Computing, The Hong Kong Polytechnic University ‡JD AI Research †{csryang, csjcao, cszwen}@comp.polyu.edu.hk ‡{wuyouzheng1, xiaodong.he}@jd.com Abstract variation), different raters scoring the same essay may assign different scores (inter-rater variation) Automated Essay Scoring (AES) is a critical (Smolentzov, 2013). To alleviate teachers’ bur- text regression task that automatically assigns den and avoid intra-rater variation, as well as inter- scores to essays based on their writing qual- ity. Recently, the performance of sentence rater variation, AES is necessary and essential. An prediction tasks has been largely improved by early AES system, e-rater (Chodorow and Burstein, using Pre-trained Language Models via fus- 2004), has been used to score TOEFL writings. ing representations from different layers, con- Recently, large pre-trained language models, structing an auxiliary sentence, using multi- such as GPT (Radford et al., 2018), BERT (Devlin task learning, etc. However, to solve the AES et al., 2019), XLNet (Yang et al., 2019), etc. have task, previous works utilize shallow neural net- shown the extraordinary ability of representation works to learn essay representations and con- strain calculated scores with regression loss or and generalization. These models have gained bet- ranking loss, respectively. Since shallow neu- ter performance in lots of downstream tasks such as ral networks trained on limited samples show text classification and regression. There are many poor performance to capture deep semantic of new approaches to fine-tune pre-trained language texts.
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