论文标题
自动简短答案分级的预算转移学习模型的比较评估
Comparative Evaluation of Pretrained Transfer Learning Models on Automatic Short Answer Grading
论文作者
论文摘要
自动简短答案分级（ASAG）是通过计算方法和所需答案对学生答案进行评分的过程。以前的作品实施了概念映射，刻面映射的方法，以及一些使用常规的单词嵌入来提取语义特征。他们手动提取多个功能以在相应的数据集上训练。我们使用转移学习模型（ELMO，BERT，GPT和GPT-2）的预预性嵌入来评估其在此任务上的效率。我们从这些模型的嵌入中提取的单个特征余弦相似性训练。我们将四个模型的RMSE分数和相关测量与Mohler数据集上的先前作品进行了比较。我们的工作表明，Elmo的表现优于其他三个模型。我们还简要描述了四个转移学习模型，并以转移学习模型结果不佳的可能原因结论。
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.
