# Automatic Essay Evaluation Technologies in Chinese Writing--A Systematic Literature Review
## 4 Discussion  
This section will address the research questions by discussing the gap between current status and desired outcomes. RQ1: What is the status of construction and usage of corpora in ACEE research? As the foundation of ACEE research, publicly accessible Chinese essay corpora are currently insufficient. Notably, there are challenges in obtaining local student essay corpora across different educational stages, which restricts the flourishing development of ACEE research. The quantity and quality of research corpora significantly impact research outcomes. In annotating high-quality essay corpora, at least two experienced raters must undergo training or calibration based on the scoring criteria. They then invest considerable time and effort in evaluating the essays. In some studies, issues such as less rigorous manual rating processes and insufficient corpus quantity have affected the accuracy and reliability of the scoring results to varying degrees. Although some research has addressed the scoring issue in small datasets to some extent through pre-trained transfer learning methods, the construction of open essay corpora can provide researchers with high-quality research resources, a unified research platform, and an active research community, according to the experience in English AEE research. RQ2: What features can ACEE extract for essay evaluation, and what are the key techniques and methods involved in the extraction process? Feature engineering in ACEE includes feature selection and extraction, essential for constructing scoring models and providing a data basis for specific feedback and suggestions in evaluation results. The development of Chinese NLP technology enables ACEE language features to gradually evolve from statistics and distributions-based shallow quantitative features to deeper language features incorporating more semantic and contextual information. The types of features include length, vocabulary classification, writing style, grammatical errors, topic relevance, discourse structure, rhetoric, etc. Feature extraction methods include statistical-based methods, rule-based methods, data-driven methods, etc. Some text analysis tools can assist in extracting some shallow features, such as the Chinese version Coh-Metrix [47; 49; 51] and the Language Technology Platform (LTP) [49; 51] developed by the Harbin Institute of Technology. However, there are still limitations in the extracted features for essay evaluation tasks. Firstly, the Chinese language has a flexible grammatical structure, which makes it challenging to use syntactic parsers to check for grammar errors. Although deep learning-based sequence labeling techniques become the mainstream Chinese grammar error detection method in recent years, the performance of the leading techniques in this task remains relatively low based on the latest evaluation results from CGED. These techniques are currently only of reference value in practical applications. Secondly, current techniques mostly rely on indirect and macroscopic features to evaluate content quality related to semantics, topics, and discourse. These features perform well in scoring tasks but only support overall comments, which are not conducive to generating specific diagnostic feedback. Thirdly, existing research is lacking in identifying and extracting certain implicit features, such as the quality of rhetoric, the depth of expressing thoughts, and high-level thinking in automated evaluation methods. Lastly, there is a lack of feature extraction for special linguistic styles in Chinese writing, such as classical Chinese, poetry, and other rhythmic genres. RQ3: What are the key technologies and methods for constructing the ACEE scoring model? The scoring task is one of the core issues in ACEE research. The methods for constructing scoring models can be broadly classified into traditional machine learning methods and deep learning methods.