# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
###### Abstract  
With the rise of artificial intelligence, conversational agents (CA) have found use in various applications in the commerce and service industries. In recent years, many conversational datasets have becomes publicly available, most relating to open-domain social conversations. However, it is difficult to obtain domain-specific or language-specific conversational datasets. This work focused on developing conversational systems based on the Chinese corpus over military scenarios. The soldier will need information regarding their surroundings and orders to carry out their mission in an unfamiliar environment. Additionally, using a conversational military agent will help soldiers obtain immediate and relevant responses while reducing labor and cost requirements when performing repetitive tasks. This paper proposes a system architecture for conversational military agents based on natural language understanding (NLU) and natural language generation (NLG). The NLU phase comprises two tasks: intent detection and slot filling. Detecting intent and filling slots involves predicting the user's intent and extracting related entities. The goal of the NLG phase, in contrast, is to provide answers or ask questions to clarify the user's needs. In this study, the military training task was when soldiers sought information via a conversational agent during the mission. In summary, we provide a practical approach to enabling  
that can be immersed in simulations will be an invaluable weapon for the Navy and Marine Corps. To reduce the risk to personnel in practice, since 2021, the U.S. The Navy has been planning to develop virtual assistants to assist in submarine hunting ([[https://voicebot.ai/2021/02/10/the-us-navy-wants-a-virtual-assistant-to-help-hunt-submarines/](https://voicebot.ai/2021/02/10/the-us-navy-wants-a-virtual-assistant-to-help-hunt-submarines/)]([https://voicebot.ai/2021/02/10/the-us-navy-wants-a-virtual-assistant-to-help-hunt-submarines/](https://voicebot.ai/2021/02/10/the-us-navy-wants-a-virtual-assistant-to-help-hunt-submarines/)) (accessed on 31 January 2022)). For example, sonar operators on ships must manage the complexity of sonar technology and set settings based on weather, location, etc. Hence, the Navy wants to utilize artificial intelligence to enhance the operating system, improving sonar detection and reducing training costs. These information-processing AI systems can be tailored to specific industries. In a recent study, the three main types of Human-Machine Interfaces (HMI) were text-based systems, voice-based systems, and interactive interface systems [17; 18; 19]. For example, Dr. [PERSON] from the Army Research Laboratory worked with the U.S. Army Combat Capabilities Development Command and the University of Southern California's Institute for creative technologies to develop autonomous systems ([[https://eandt.theiet.org/content/articles/20](https://eandt.theiet.org/content/articles/20)]([https://eandt.theiet.org/content/articles/20](https://eandt.theiet.org/content/articles/20)) 21/04/military-bots-could-become-teammates-with-real-time-conversational-ai/ (accessed on 31 January 2022) [20] to derive intent from a soldier's speech via a statistical language classifier. By combining NLU with dialogue management and having the classifier learn the patterns between verbal commands, responses, and actions, they created a system that could respond appropriately to new commands and knew when to request extra information. In addition, [PERSON] et al. [21] proposed a conversational multimodal interface by combining visual indicators with a conversational system, providing a natural way for users to gain information on vehicle status/faults and mission progress and to set reminders. The system can be used for operations in remote and hazardous environments. During military training missions, soldiers must follow guidelines or personnel instructions. However, the overloading information may not be understood and completed effectively.