  Automated essay scoring plays an important role in judging students' language
abilities in education. Traditional approaches use handcrafted features to
score and are time-consuming and complicated. Recently, neural network
approaches have improved performance without any feature engineering. Unlike
other natural language processing tasks, only a small number of datasets are
publicly available for automated essay scoring, and the size of the dataset is
not sufficiently large. Considering that the performance of a neural network is
closely related to the size of the dataset, the lack of data limits the
performance improvement of the automated essay scoring model. In this paper, we
proposed a method to increase the number of essay-score pairs using
back-translation and score adjustment and applied it to the Automated Student
Assessment Prize dataset for augmentation. We evaluated the effectiveness of
the augmented data using models from prior work. In addition, performance was
evaluated in a model using long short-term memory, which is widely used for
automated essay scoring. The performance of the models was improved by using
augmented data to train the models.
