## 1 Introduction
Three types of NQG models developed through the fine-tuning of BERT, including BERT-QG, BERT-SQG (sequential QG), and BERT-HLSQG (high-light sequential QG), have been proposed by [PERSON] et al. [17]. Moreover, an NQG model incorporating a fine-tuned GPT-2 model was proposed by [PERSON] et al. [18]. MixQG, a QG model that combines nine question answering datasets containing various answer types and applies them to T5 and BART, was proposed by [PERSON] et al. [19]. As a result, it was possible to generate queries with different cognitive levels under different answer type conditions, and about 10% performance improvement was demonstrated compared with the T5 and BART algorithms. To ensure the complexity and quality of the question, [PERSON] et al. extracted key entities using a Graph Attention Network (GAT) and proposed CQG, a QG model that applied BERT and flag tags using the extracted key entities [20]. As a result of the HotpotQA performance experiment, it proved to have about 25% performance improvement compared with the existing model at 5 BLEU. [PERSON] et al. proposed a QG model that uses an article to summarize, and it applies to the BART model [21]. The model was able to apply tasks simultaneously as a supervised model and an unsupervised model and demonstrated higher performance compared with the existing model. As such, it can be seen that research on transformer-based NQG models is being actively conducted. Additionally, the ensemble strategy for solving the challenge of answering questions in natural language based on the attention mechanism emerged. To combine the question answering, [PERSON] et al. employed the Bidirectional Attention Flow (BiDAF), QANet, and Mnemonic Reader models [22; 23; 24; 25]. Following the acquisition of class-specific voting weights for each question word, this model is a technique of answering in which the candidate response with the highest weight is chosen from a pool of many candidates. As a result, contemporary research is being undertaken utilizing an ensemble model based on the attention mechanism. Unlike BERT or GPT, which use either the encoder block or decoder block of the transformer but not both, the text-to-text transfer transformer (T5) model simultaneously uses the encoder block and the decoder block, hence being adequate for application to all NLP tasks. In addition, its recent use in various NLP fields has confirmed high performance in comparison to other models [26]. In this paper, we propose an algorithm to construct a dataset that generates a large number of questions of high accuracy to train a chatbot service using a T5-based ensemble model (Ensemble-NQG-T5). In specific, when a representative question is selected among questions extracted from multiple NQG models of high similarity, a bias occurs due to high redundancy while training the chatbot algorithm. The issue, however, can be overcome using the proposed method. Utilization of the soft-voting method for questions generated between each NQG model ensures a high similarity with the input text, a low correlation between the generated questions, and concomitantly increases the number of generated questions, hence being suitable for constructing a PTM learning dataset. To do so, the average of the Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores from each NQG model is selected as weights [27; 28]. Then, candidate questions are generated for each model, and weight information calculated in advance for each model is assigned to the generated questions. Subsequently, the relevance of each question with other generated questions is calculated, and questions with a similarity exceeding the threshold are considered the same question. Whether to select a question is finally decided through a soft-voting method that adopts a question generated from a high-accuracy model according to the initial assigned weight. Consequently, compared with the single NQG method, the quantity and quality of questions can be enhanced.