# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
###### Abstract
In addition, traditional retrieval systems may delay user action. Hence, we constructed a conversational agent over a set of military scenarios that enables users to operate on constantly evolving battlefields and to obtain the information they need through conversation. Based on the survey of conversational systems in [22], we aim to design task-oriented dialogue systems for application in military scenarios, focusing on question answering with martial training intent and relevant entity information. Therefore, conversational goals for social and entertainment purposes, such as greetings, entertainment, and advertising, are outside the focus of our system. Therefore, the ability of a military conversational AI to correctly detect its user's intent and identify entities in a sentence will determine whether it can successfully reply to users. One challenge for intent detection is that the questions of military users can be terse and ambiguous [23]. Furthermore, the answer often depends on the context of the conversations. To narrow down the range of possible intent types, we first defined the range of applications for the types of intent it was meant to detect, and then classified and annotated the conversational data. In general, although the users' queries are short, most will mention the important entities. The role of slot filling is to identify and annotate the entities in the sentence, e.g., persons, events, times, locations, and weapons. As for the response provided by the system to the user, the challenges are to choose the most appropriate answer and to generate questions that require explicit information when missing the primary entity from the user's query. To enable the task-oriented conversational system, the architecture comprises four modules based on a pipeline strategy: (1) slot filling, (2) intent detection, (3) retrieval-based answering, and (4) query generation. For the (1) and (2) modules, we trained by our prepared dataset by named-entity recognition (NER) models and a support vector machine (SVM) classifier [24], respectively. The (3) module is used by the BM25 algorithm [25] to retrieve the Military List and then rank the most appropriate solution using the Learning to Rank (LTR) model [26]. For the final module, we adopted the template-based question generation for the database of the Army Joint Task List (AJTL) according to the user's intent. The performance of our military conversational system was experimentally evaluated in terms of the performance of its intent detection, slot filling, LTR modeling, and question generation modules. The system performed well based on both quantitative and qualitative metrics. Therefore, this study established a new approach for the development of military conversational systems. The proposed architecture could also be trained using other domain-specific datasets to expand its scope of applicability. The contributions of this study may be summarized as follows. * A task-oriented conversational system was designed based on the practical needs of military tasks. As its module functions and datasets are mutually independent, it is possible to use this architecture to accelerate the training of domain-specific conversational systems in other domains, as one simply has to replace the dataset. * This study defined the four core tasks of a conversational system and used machine-learning technologies to enable the realization. They included using NER models for slot filling, a classifier for intent detection, answering by the retrieval-based and learning-to-rank (LTR) model, and generating new queries by the template-based method. * The experimental results highlight the performance of the intention detection, slot filling, sentence ranking, and the overall user satisfaction for the conversational system. The result can serve as a promising direction for future studies. The remainder of this paper is organized as follows. Section 2 describes related work and technologies. Section 3 introduces the proposed architecture and functions.