An Automated Grader for Chinese Essay Combining Shallow and Deep Semantic Attributes

Writing is a pivotal part of the language exam, which is considered as a useful tool to accurately reflect students' language competence. As Chinese language tests become popular, manual grading becomes a heavy and expensive task for language test organizers. In the past years, there is a large volume of research about the automated English evaluation systems. Nevertheless, since the Chinese text has more complex grammar and structure, much fewer studies have been investigated on automated Chinese evaluation systems. In this paper, we propose an automated Chinese essay evaluation system called AGCE (Automated Grader for Chinese Essay), which combines shallow and deep semantic attributes of essays. We implement and train our AGCE system on a Chinese essay dataset, which is created by ourselves based on more than 1000 student essays from a Chinese primary school. Experimental results indicate that our AGCE system achieves the quadratic weighted Kappa of 0.7590 on a small dataset, which is of higher grading accuracy compared with other four popular neural network methods trained on large-scale datasets. In addition, our AGCE system can provide constructive feedback about Chinese writing, such as misspelling feedback and grammatical feedback about writers' essays, which is helpful to improve their writing capability.