A Study of Distributed Semantic Representations for Automated Essay Scoring

摘要

Automated essay scoring (AES) applies machine learning and NLP techniques to automatically rate essays written in an educational setting, by which the workload of human raters is considerably reduced. Current AES systems utilize common text features such as essay length, tf-idf weight, and the number of grammar errors to learn a scoring function. Despite the effectiveness brought by those common features, the semantics within the essay text is not well considered. To this end, this paper presents a study of the usefulness of the distributed semantic representations to AES. Novel features based on word or paragraph embeddings are combined with the common text features in order to improve the effectiveness of the AES systems. Evaluation results show that the use of the distributed semantic representations are beneficial for the task of AES.