## 4 Experimental Results and Discussion
### Results  
Table 2 depicts performance evaluation results of each model used for Ensemble-NQG-T5, including BLEU-N and ROUGE-L scores, along with the number of questions generated using the SQuAD 2.0. Results confirm that the BLEU-N and ROUGE-L scores of the T5-Base-based model were observed to be relatively higher than that of the T5-Small-based model due to differing pretraining sizes: For a single task, the BLEU-N score was higher by 0.029, while for the ROUGE-L score it was by 0.014. For multitask, the BLEU-N score was  
higher by 0.017, while for the ROUGE-L score it was by 0.014. Finally, for the end-to-end approach, the BLEU-N score was higher by 0.045, while the ROUGE-L score was by 0.018.  
Regarding the quantity of QG, a larger number of questions were generated in the case of multitask. Specifically, for the T5-Small model, 5 more questions were generated in the case of multitask, and for the T5-Base model, 29 more questions were generated for multitask. Conversely, the end-to-end approach resulted in the generation of 290 more questions when incorporating the T5-Small model compared with the T5-Base model.  
Table 3 describes results of ensemble optimization using questions generated from Ensemble-NQG-T5. For each category, the number of generated questions and the BLEU-N and ROUGE-L scores were compared when assigned different MST. At a low MST, even if the similarity between generated questions was low, only one representative question was extracted. Therefore, the resulting BLEU-N and ROUGE-L scores were high, at 0.556 and 0.595, respectively. However, only 415 questions could be extracted. On the other hand, at a high MST, only one representative question was extracted when the similarity between the questions was high. Hence, both the BLEU-N and ROUGE-L scores were observed to be low, at 0.462 and 0.427, respectively. Conversely, a large number of 5123 questions was derived.  
In other words, as a trade-off relationship exists between BLEU-N and ROUGE-L scores and the number of generated questions, specific results can be derived according to the user's needs by adjusting the MST.  
Figure 4 depicts comparative results of evaluations on the Ensemble-NQG-T5 and single model results. For Ensemble-NQG-T5, the average evaluation score increased by up to 29.7 percent, and the amount of questions generated increased by up to 607.0% compared with the single T5 model results. Consequently, using this proposed algorithm, it is possible to create a question dataset for chatbots with higher quality and quantity than a single model, taking into account the characteristics of the amount of generated questions and mutual similarity.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Number** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline BLEU-N & 0.447 & 0.465 & 0.476 & **0.482** & 0.426 & 0.471 \\ ROUGE-L & 0.422 & 0.427 & 0.436 & **0.441** & 0.407 & 0.425 \\ \# of GQ & 767 & 772 & 778 & 807 & **1117** & 827 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance Evaluation for Each Single T5 Model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mutual Similarity Threshold** & **BLEU-N** & **ROUGE-L** & **\# of GQ** \\ \hline
0.1 & **0.556** & 0.595 & 415 \\
0.2 & 0.555 & **0.599** & 617 \\
0.3 & 0.546 & 0.552 & 963 \\
0.4 & 0.517 & 0.505 & 1303 \\
0.5 & 0.522 & 0.473 & 1708 \\
0.6 & 0.517 & 0.445 & 2055 \\
0.7 & 0.490 & 0.429 & 2480 \\
0.8 & 0.472 & 0.421 & 2945 \\
0.9 & 0.461 & 0.416 & 3458 \\
1.0 & 0.462 & 0.427 & **5123** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance Evaluation of Ensemble-NQG-T5 by Mutual Similarity Threshold.