## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Pre-Processing**} & \multicolumn{2}{c}{**Gradient**} & \multirow{2}{*}{**Split Size**} & \multirow{2}{*}{**Batch Size**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Pearson Correlation**} & \multicolumn{1}{c}{**Average Runtime**} \\  & & & & & & & & & & & \\ \hline \multirow{3}{*}{all-distilroberta-v1} & SEB + Aug3.5 & SpCH + LC & Yes & 80-20 * & 16 & 0.8857 & 0.8186 & 0.8236 & 0.6399 & 1066 s \\  & SEB + Aug4 & SpCH & & & 0.8205 & 0.8556 & 0.8556 & 0.8556 & 0.7103 & 1406 s \\  & SEB + Aug4 & SpCH & & & 0.7429 & 0.8375 & 0.8377 & 0.6738 & 1390 s \\ \hline \multirow{3}{*}{multi-qa-mpnet-base-dot-v1} & SEB + Aug3.5 & SpCH + LC & Yes & 80-20 * & 16 & 0.7281 & 0.8301 & 0.8296 & 0.6559 & 1806 s \\  & SEB + Aug4 & SpCH & & & 0.7329 & 0.8607 & 0.8507 & 0.6909 & 2285 s \\  & SEB + Aug4 & SpCH & & & 0.7956 & 0.8392 & 0.8393 & 0.6765 & 2285 s \\ \hline \multirow{3}{*}{RoBERTa-large} & SEB + Aug3.5 & SpCH + LC & Yes & 80-20 * & 16 & 0.8648 & 0.8722 & 0.8722 & 0.7411 & 6381 s \\  & SEB + Aug3.5 & SpCH + LC & Yes & 80-20 * & 16 & 1.0093 & 0.8657 & 0.8658 & 0.7319 & 6769 s \\ \hline \multirow{3}{*}{all-distilroberta-v1} & SEB + Aug3.5 & SpCH + LC & No & 80-20 * & 16 & 1.1348 & 0.8358 & 0.8357 & 0.6668 & 909 s \\  & SEB + Aug4 & SpCH & & & 1.1527 & 0.8313 & 0.8312 & 0.6582 & 1114 s \\ \hline \multirow{3}{*}{multi-qa-mpnet-base-dot-v1} & SEB + Aug3.5 & SpCH + LC & No & 80-20 * & 16 & 0.8216 & 0.8215 & 0.8519 & 0.6928 & 1198 s \\  & SEB + Aug4 & SpCH & & & 0.8161 & 0.8639 & 0.8636 & 0.7265 & 2409 s \\ \hline \multirow{3}{*}{Graph Convolutional Networks [29]} & SEB & - & - & 90-10 & 32 & - & 0.705 & 0.710 & - & - \\  & SEB + BT & - & - & 90-10 & 32 & - & 0.725 & 0.732 & - & - \\ \cline{1-1}  & SEB & - & - & 90-10 & 16 & - & 0.693 & 0.702 & - & - \\ \hline \hline \end{tabular}
* Exp 3,
* Exp 5, BT: back translation.  
\end{table} 
Table 8: Comparison of additional experimental results with previous research. could explore the implementation of models trained on datasets from other languages. GPT has proven to be able to generate new sentences to enhance performance. Therefore, it is also possible to leverage GPT for language translation when using datasets other than in English. Conceptualization, M.C.W. and H.-S.Y. ; methodology, M.C.W. ; software, M.C.W. ; validation, H.-S.Y. ; formal analysis, M.C.W. ; investigation, M.C.W. ; resources, M.C.W. ; data curation, M.C.W. ; writing--original draft preparation, M.C.W. ; writing--review and editing, M.C.W. and H.-S.Y. ; visualization, M.C.W. ; supervision, H.-S.Y. All authors have read and agreed to the published version of the manuscript. This research is partially supported by a Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure, and Transport (Grant RS-2022-00143782). Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.