Automatic Essay Evaluation Technologies in Chinese Writing—A Systematic Literature Review

Abstract

Automatic essay evaluation, an essential application of natural language processing (NLP) technology in education, has been increasingly employed in writing instruction and language proficiency assessment. Because automatic Chinese Essay Evaluation (ACEE) has made some breakthroughs due to the rapid development of upstream Chinese NLP technology, many evaluation tools have been applied in teaching practice and high-risk evaluation processes. However, the development of ACEE is still in its early stages, with many technical bottlenecks and challenges. This paper systematically explores the current research status of corpus construction, feature engineering, and scoring models in ACEE through literature to provide a technical perspective for stakeholders in the ACEE research field. Literature research has shown that constructing the ACEE public corpus is insufficient and lacks an effective platform to promote the development of ACEE research. Various shallow and deep features can be extracted using statistical and NLP techniques in ACEE. However, there are still substantial limitations in extracting grammatical errors and features related to syntax and traditional Chinese Literary style. For the construction of scoring models, existing studies have shown that traditional machine learning and deep learning methods each have advantages in different corpora and feature selections. The deep learning model, which exhibits strong adaptability and multi-task joint learning potential, has broader development space regarding model scalability.