预训练迁移学习模型在简答题自动评分中的比较评价
Comparative Evaluation of Pretrained Transfer Learning Models on Automatic Short Answer Grading
摘要：
自动简短答案评分（ASAG）是通过给定问题和期望答案的计算方法对学生答案进行评分的过程。以往的工作实现了概念映射，刻面映射的方法，和一些使用传统的词嵌入提取语义特征。他们手动提取多个特征，在相应的数据集上进行训练。我们使用迁移学习模型ELMo、BERT、GPT和GPT-2的预训练嵌入来评估它们在这项任务上的效率。我们用从这些模型的嵌入中提取的单一特征余弦相似度进行训练。我们比较了四个模型的RMSE分数和相关性测量与以前的工作Mohler数据集。我们的工作表明，ELMo优于其他三个模型。我们还简要介绍了四种迁移学习模型，并总结了迁移学习模型效果不佳的可能原因。
英文摘要：
Automatic Short Answer Grading (ASAG) is the process of grading the student answers by computational approaches given a question and the desired answer. Previous works implemented the methods of concept mapping, facet mapping, and some used the conventional word embeddings for extracting semantic features. They extracted multiple features manually to train on the corresponding datasets. We use pretrained embeddings of the transfer learning models, ELMo, BERT, GPT, and GPT-2 to assess their efficiency on this task. We train with a single feature, cosine similarity, extracted from the embeddings of these models. We compare the RMSE scores and correlation measurements of the four models with previous works on Mohler dataset. Our work demonstrates that ELMo outperformed the other three models. We also, briefly describe the four transfer learning models and conclude with the possible causes of poor results of transfer learning models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
