# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
## 5 Conclusions  
Conversational AI has found commercial applications in entertainment, food, and medicine. However, relatively little research has been conducted on AI applied to military dialogue. One of the challenges this study faces is that a large number of Chinese training datasets are not easy to obtain, and the existing research mainly uses English public social training datasets. Another challenge is to consider the practice of the whole system, which comprises several modules. In contrast, many studies have focused on improving several specific modules (NLU or NLG). The main contribution of this work is to combine multiple research topics into one framework, including intent detection, slot filling, and response generation. We applied various machine-learning techniques, including filling slots with NER models, intent detection with classifiers, answering with retrieval and learned-to-rank (LTR) models, and template-based methods to generate new queries. We design a task-oriented conversational system according to the actual needs of military missions. Since its module functions and datasets are independent of each other, this architecture can accelerate the training of problem-specific conversational systems in other service domains since only the datasets need to be replaced. Each method module can also be further considered to be replaced by methods with higher performance or efficiency in the future for comparison. From the evaluation results of the experiment, it is feasible to realize the application of dialogue AI in military scenarios based on intent detection and response generation technology. The experimental results show that the query satisfaction in eight scenarios is greater than 80% after two rounds of dialogue based on retrieval-based response generation. We integrated technologies such as natural language processing, information retrieval, and natural language generation, and used the limited military corpus to achieve the expected preliminary results of the plan. Through dialogue AI, we can help military trainers conduct multi-round question-and-answer sessions. In future work, this research could improve in two directions: (1) Considering the amount of data and the feasibility of integrating multiple system modules, we choose the CRF-based method and SVM as the baseline for NLU tasks. This study has used the limited military conversational dataset to implement a conversational AI system for military training scenarios. In spite of this, using the CRF and SVM models are indeed preliminary approaches to implementation. During future research, we would like to apply transfer learning to expand multiple military domains with small datasets and apply few-shot learning to enhance performance. (2) Applying deep-learning architectures to replace template-based question-generation methods improves their accuracy and language expressiveness. Although current template-based queries have no obvious semantic problems, the generated sentence patterns are limited. Functional expansion based on the above two directions enables the system to take the proposal as a whole. In addition, in this military training scenario, we plan to use distant supervision to automatically label our data to expand the number of datasets and improve model training accuracy. Finally, we plan to study the feasibility of integrating Semantic Web technologies. Knowledge representation and reasoning and the construction of sentence generation using knowledge graphs architectures based on these techniques are refined to improve the NLG process of conversational AI systems. H.-M.C. conducted conceptualization, methodology, investigation, writing-original draft preparation, review & editing, supervision. D.-W.C. conducted data curation, software. All authors have read and agreed to the published version of the manuscript. This research is sponsored by the Ministry of Science and Technology, Taiwan, under grant MOST 108-2221-E-606-013-MY2. Not applicable.