@techreport{oai:ipsj.ixsq.nii.ac.jp:00216464,
 author = {Bo, Wang and Tsunenori, Ishioka and Tsunenori, Mine and Bo, Wang and Tsunenori, Ishioka and Tsunenori, Mine},
 issue = {11},
 month = {Feb},
 note = {Large-scaled encoders such as BERT have been actively used for sentence embedding in automatic scoring. However, the embedding may not be optimal due to non-uniform vector distribution. By conducting fast contrastive learning, methods like SBERT got better semantic embeddings and were actively used in textual similarity datasets. However, the cost to obtain the similarities limits its application to automatic grading. In this paper, we propose a method of calculating similarity from the rubric to perform contrastive learning for a better semantic embedding. We conducted extensive experiments on 60,000 answer/question data for three independent questions. The experimental results show that the proposed method outperforms all baselines in terms of accuracy and computation time., Large-scaled encoders such as BERT have been actively used for sentence embedding in automatic scoring. However, the embedding may not be optimal due to non-uniform vector distribution. By conducting fast contrastive learning, methods like SBERT got better semantic embeddings and were actively used in textual similarity datasets. However, the cost to obtain the similarities limits its application to automatic grading. In this paper, we propose a method of calculating similarity from the rubric to perform contrastive learning for a better semantic embedding. We conducted extensive experiments on 60,000 answer/question data for three independent questions. The experimental results show that the proposed method outperforms all baselines in terms of accuracy and computation time.},
 title = {Automatic Short Answer Grading with Rubric-based Semantic Embedding Optimization},
 year = {2022}
}