## 3 Ensemble-NQG-T5  
### Overview  
Figure 2 depicts the architecture of Ensemble-NQG-T5 for different fine-tuned models to NQG function-based T5. First, questions are generated for each NQG model using the SQuAD context dataset, and model evaluation scores for QG from the each model (BLEU, ROUGE) are calculated. Then, the accuracy weights of each question are calculated. Subsequently, candidate questions are generated for each model by using the target context for QG as input. Generated questions are assigned weight information calculated in advance for each generated model. Then, the similarity between questions is calculated in a round-robin manner. Questions possessing a similarity exceeding the threshold are considered identical, and questions generated from a model with higher accuracy are adopted according to preassigned weights. Subsequently, soft-voting classification is performed, where the remaining questions are discarded. Therefore, a question dataset may be generated for each purpose by optimizing the similarity between questions and the quantity of generation.  
First, each NQG model generating a question using the input context was fine-tuned to the output form of QG based on T5 [37; 38]. During the fine-tuning process, distinct parameters are set and learned for each NQG model. Hence, the questions generated from each model are expressed differently, resulting in distinct QG performance [39]. Table 1 depicts the NQG parameters for each model. The NQG model of the Single Task type uses the Context \((c)\) as an input, and a dictionary of QA pairs is generated as the output. Apart from output in the form of a dictionary, the multitask-type NQG model also includes a function to produce answers \((a)\) as an output in response to inputs of question \((q)\) and context pairs. Finally, the NQG model of the end-to-end type takes the Context as an input and generates only questions without answer supervision. Therefore, when comparing the quantity of QG using the same context, the end-to-end-type NQG model is most optimal. However, since questions are generated in the absence of supervision on the answer, the accuracy of the generated query is relatively low. Consequently, although a large amount of data to learn the algorithm can be guaranteed, the reliability of the chatbot's accuracy post-learning is low.  
Figure 2: Architecture of Ensemble-NQG-T5.  
Therefore, in this study, an ensemble model (Ensemble-NQG-T5) based on different T5 models is presented, and through a soft-voting method between the datasets of the generated questions, the accuracy and quantity of QG are improved compared with those of the single-model-based NQG.  
Detailed descriptions of Ensemble-NQG-T5, depicted in Figure 3, are as follows: First, SQuAD is used to generate model-specific questions from the input context for six pretrained and fine-tuned NQG-T5 models, each of different forms. Since model and task types differ, questions are created in the context of the same declarative text. Hence, even if the order of the words is fixed, a variety of question types with similar contents may be generated through the addition or deletion of words. For each model-specific question generated, BLEU and ROUGE scores are calculated in relationship with the target context, which is subsequently used as the weight of Ensemble-NQG-T5.