  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.
