# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
## 2 Related Work
### Conversational AI  
The rapid development of AI technologies has increased academic interest in human-computer interfaces, with applications ranging from domain-specific settings to open-domain conversations. In the business world, personalized AI assistants such as Siri (Apple), Assistant (Google), Cortana (Microsoft), Messenger (Facebook), and Alexa (Amazon) have become increasingly common. Owing to the extensive labeling of conversational databases and the application of deep-learning and NLP techniques, conversational systems have made considerable progress in understanding the semantics of natural language and contextual reasoning. We divide conversational AI into several categories according to their purposes as the following. Conversational AI, which are also known as chatbots, may be divided into task-oriented or non-task-oriented dialogue systems [27]. Task-oriented dialogue systems are meant to help users perform a specific task, e.g., intelligent food ordering, legal queries [28], and smart customer service. In addition, they usually have domain-specific conversational dialogues and knowledge bases. Non-task-oriented dialogue systems (e.g., chatbots for the elderly or children) are meant to provide reasonable responses to users and thus provide entertainment and have open-domain dialogues that are not specifically constrained in scope. The first chatbot in the world was the Eliza chatbot in 1996 [29], which used simple dialogue to mimic a psychologist conversing with a patient. In 2017, [PERSON] et al., developed Woebot [30], a cognitive-behavioral therapeutic (CBT) chatbot, which was able to converse with patients and provide CBT assistance. [PERSON] et al. [31] proposed a unified conversational search/recommendation framework called \"System Ask--User Respond,\" which was trained using a large collection of user reviews in e-commerce. They then evaluated the performance of this framework using metrics such as the Normalized Discounted Cumulative Gain (NDCG). Task-oriented dialogue systems are typically designed with a \"pipeline\" consisting of four modules: a NLU module, dialogue state tracker, dialogue policy learning module, and NLG module [27]. Recently, some workers have proposed end-to-end frameworksto expand the expressiveness of the state space and support dialogue beyond domain-specific corpora [32]. For example, [PERSON] and [PERSON] proposed an end-to-end framework that used reinforcement learning and policy learning to optimize the dialogue system for dialogue state tracking. They tested this framework using a 20-question game, where the conversational system asked the user a series of yes-or-no questions to find the answer to a specific question. There are three main approaches for conversational response generation [4]: rule-based approaches, retrieval-based approaches, and generative approaches. A rule-based system often requires a large amount of manual design and labeling work and therefore has the highest costs. Retrieval-based conversational AI uses keyword matching with machine learning or deep learning to determine an optimal predefined response [33]. Finally, generative conversational AI can be trained in multiple stages using supervised/unsupervised learning, reinforcement learning, or adversarial learning. Recently, [PERSON] et al. [34] presented a graph-based self-adaptive conversational AI; it used a knowledge graph whose nodes and links represented key entities and semantic relationships, respectively, as a dynamic knowledge base. It allowed the system to gain knowledge through conversations with end-users. Based on the definition above and the category of conversational AI, the system presented in this work may be characterized as a domain-specific (military) task-oriented dialogue system. To ensure that the dialogues produced by the conversational AI are compatible with the expectations of military tasks, we used a pipeline design and retrieval-based response generation method.