Improving Performance of Automated Essay Scoring by using back-translation essays and adjusted scores
This work addresses data scarcity for automated essay scoring in education, but it is incremental as it applies existing augmentation techniques to a specific domain.
The paper tackled the problem of limited dataset size in automated essay scoring by using back-translation and score adjustment to augment essay-score pairs, resulting in improved model performance when evaluated on prior models and an LSTM-based model.
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.