# Automatic Essay Evaluation Technologies in Chinese Writing--A Systematic Literature Review
## 3 Results
### Scoring Model (RQ3)
#### 3.3.4 Comparative Analysis of Scoring Models
[47], which shows a lower PCC value, possibly due to insufficient data and limitations in the selected features), reflecting the current state-of-the-art performance benchmark of deep learning models in ACEE scoring. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multicolumn{3}{c}{**Corpus**} & \multicolumn{3}{c}{**Features**} & \multicolumn{3}{c}{**Evaluate Results**} \\ \cline{2-10}  & **Category** & **Size** & **S** & **D** & **E** & & & **AR** & **EAR** & **PCC** & **QWK** \\ \hline
[18] (2004) & HSK & 100/140 & ✓ & & & MLR & & & & TP. 076/Cr 0.714 & \\
[33] (2014) & HSK & 1523/9073 & ✓ & & & MLR & & & & \\
[35] (2021) & HSK & 102/27 & ✓ & ✓ & ✓ & Ordered Logistic Regression & & & 0.734 & 0.714 \\
[34] (2022) & HSK & 8878 & & ✓ & DNN & & & TP. 0728/Cr 0.732 & TP.0678 Cr0.704 \\
[19] (2004) & HMK & 700 & ✓ & & MLR & & & 0.822 & \\
[37] (2011) & MHK & 994 & ✓ & & MLR & & 0.97 & & \\
[41] (2010) & MHK & 970 & & ✓ & SVR & & & & 0.611 & \\
[41] (2016) & MHK & 16,776 & ✓ & ✓ & SVM & & 0.895 & & \\
[38] (2017) & MHK & 12,600 & ✓ & ✓ & SVM & 0.897 & & & \\
[40] (2014) & MHK & 16,776 & ✓ & & SVM & 0.89 & & & \\
[43] (2012) & MHK & 8000 & ✓ & & Feature Similarity & & & & \\
[43] (2020) & NSM & 15,000 & ✓ & & MLR & & & 0.867 & \\
[51] (2022) & NSM(1) & 10,500 & ✓ & ✓ & & MLR & & 0.942 & & 0.867 \\
[52] (2020) & NSM(1) & & ✓ & ✓ & MLR & & & 0.83 & 0.535 & \\
[53] (2021) & NSM(1) & 10,300 & ✓ & ✓ & & BLRR & & & & 0.696 \\
[54] (2018) & NSM(1) & 100/500 & ✓ & ✓ & & SVR & 0.931 & & 0.83 & \\
[54] (2016) & NSM(2) & 220 & ✓ & ✓ & SVR & 0.927 & & & 0.82 & \\
[55] (2020) & NSM(1) & 693 & ✓ & ✓ & Decision Tree & 0.91 & 0.48 & & & \\
[56] (2019) & NSM(1) & 1000 & ✓ & ✓ & Random Forest & & & & 0.759 \\
[57] (2008) & NSM(1) & 202 & ✓ & ✓ & & USA & & & 0.55 & \\
[58] (2009) & NSM(1) & 689 & ✓ & ✓ & Feature Similarity & 0.92 & & & \\
[59] (2020) & NSM(1) & 689 & ✓ & ✓ & Feature Similarity & 0.84 & 0.37 & & \\
[59] (2020) & NSM(1) & 100/107 & ✓ & ✓ & ✓ & CNN & 0.799 & & 0.45 & \\
[60] (2020) & NSM(1) & 8535 + 3858 & ✓ & ✓ & ARCNN & & & 0.662 & 0.628 \\
[59] (2020) & NSM(1) & 300 & ✓ & RNN & & & & 0.642 & 0.604 \\
[45] (2022) & NSM(1) & 850 + 300\(-\)500 & ✓ & ✓ & ✓ & DNN & & 0.882 & & 0.636 \\ \hline \hline \end{tabular} Note: (1) The meaning of abbreviations in the Corpus column, NS represents local student compositions, while 2, M, H, and C in parentheses represent primary school, middle school, high school, and college, respectively, and the numbers following represent grades. S, D, and E in the Feature columns represent shallow semantic attributes, and embedded features, respectively. In Evaluation Results, TP represents the same topic, while CP represents a cross-topic. (2) In the Corpus Size column, if there is a “/ between two numbers, it indicates that two different corpora are used to train models. The numbers on both sides of “/” indicate pre-training and fine-tuning data sizes.  
\end{table} 
Table 3: The comparison of ACE scoring models.