## 5 Conclusions  
In this study, we introduced EssayGAN, which can automatically augment essays rated with specific scores. It consists of multiple generators and a discriminator. EssayGAN can generate as many essays as necessary by sampling sentences from the training set of essays rated with target scores. To the best of our knowledge, EssayGAN is the first attempt to automatically augment text data on a sentence basis. In addition, EssayGAN can maintain coherence between sentences in an augmented essay. We performed several experiments on the AES task to verify the usefulness of EssayGAN. The experimental results proved that EssayGAN is a reliable data augmentation tool for supervised learning. Therefore, EssayGAN can alleviate the problem of a lack of training data, especially when complex AES systems based on deep learning networks are required. Furthermore, EssayGAN, using multiple generators, can augment essays with a higher quality than that of a conventional GAN using a single generator. A simple AES system, even compared with the state-of-the-art AES models, can yield promising results if it can be trained on more augmented data. Our future work will include increasing the coherence levels between sentences in an augmented essay and applying EssayGAN to various applications that require augmented data consisting of multiple sentences. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Baseline**} & \multicolumn{4}{c}{**Data Augmentation (\(\times\)1)**} \\ \hline \multirow{2}{*}{**Prompt**} & **Training** & \multirow{2}{*}{**Random**} & **Random** & **EssayGAN** & \multirow{2}{*}{**EssayGAN**} & \multirow{2}{*}{**CS-GAN**} \\  & **Data** & & & & **w/o \(t\)-Step** & & \\ \hline
1 & 0.7894 & 0.7601 & 0.7663 & 0.7656 & **0.818** & - \\ \hline
2 & 0.6741 & 0.6587 & 0.6507 & 0.6918 & **0.696** & - \\ \hline
3 & 0.6796 & 0.6525 & 0.6462 & 0.6776 & **0.686** & 0.665 \\ \hline
4 & 0.8062 & 0.7695 & 0.7773 & 0.8128 & **0.828** & 0.788 \\ \hline
5 & 0.8038 & 0.7614 & 0.7723 & 0.8054 & **0.824** & 0.790 \\ \hline
6 & 0.8090 & 0.7767 & 0.7794 & 0.8166 & **0.829** & 0.799 \\ \hline
7 & 0.8271 & 0.8056 & 0.8102 & 0.8286 & **0.865** & - \\ \hline
8 & 0.6454 & 0.6989 & 0.7110 & 0.7195 & **0.761** & - \\ \hline AVG & 0.7543 & 0.7354 & 0.7392 & 0.7647 & **0.788** & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: QWK correlation comparison between different augmentation techniques. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**AES Systems**} \\ \hline \multirow{2}{*}{**Prompt**} & **BERT-CLS** & **XLNet-CLS** & **R\({}^{2}\)BERT** & **BERT** & **BERT** & **BERT-CLS** \\  & **[8]** & **[8]** & **[9]** & **-Ensemble** & **-Adapter** & **+ Augmented Data** \\  & **[10]** & **[15]** & **[15]** & **[15]** & **of EssayGAN** \\ \hline
1 & 0.792 & 0.776 & 0.817 & **0.831** & 0.743 & 0.818 \\ \hline
2 & 0.679 & 0.680 & **0.719** & 0.679 & 0.674 & 0.696 \\ \hline
3 & 0.715 & 0.692 & 0.698 & 0.690 & **0.718** & 0.686 \\ \hline
4 & 0.800 & 0.806 & 0.845 & 0.825 & **0.884** & 0.828 \\ \hline
5 & 0.805 & 0.783 & **0.841** & 0.817 & 0.834 & 0.824 \\ \hline
6 & 0.805 & 0.793 & **0.847** & 0.822 & 0.842 & 0.829 \\ \hline
7 & 0.785 & 0.786 & 0.839 & 0.841 & 0.819 & **0.865** \\ \hline
8 & 0.595 & 0.628 & 0.744 & 0.748 & 0.744 & **0.761** \\ \hline AVG & 0.748 & 0.743 & **0.794** & 0.782 & 0.785 & 0.788 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison with the state-of-the-art AES systems from the literature. **Author Contributions:** Conceptualization, C.-Y.P. and K.-J.L. ; methodology, Y.-H.P. and Y.-S.C.; software, Y.-H.P. ; validation, Y.-H.P. ; writing--original draft preparation, K.-J.L. and Y.-H.P. ; writing--review and editing, K.-J.L. and Y.-H.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable.