# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
## 2 Related Work
### Intent Detection and Slot Filling  
In a conversation, the queries provided by the user are usually relatively short. Therefore, the dialogue system must first determine the aim or intent of the question. However, the information within the query may be incomplete or stated implicitly. Suppose the dialogue system does not possess the knowledge or context necessary to answer the question. In that case, it may require several rounds of dialogue to redress these issues and confirm the user's intentions. In 2018, [PERSON] et al. [31] proposed a \"circular\" conversational architecture, where the dialogue system clarified a user's intentions through several rounds of dialogue. Intent detection pertains to the determination of intent by analyzing the structure of the question [37], e.g., by \"5W2H\" analysis (why, what, where, when, who, how, and how much) [38], to improve the accuracy of the retrieved answer. Furthermore, as the information contained by the query is critical for determining the correct answer, NER techniques can be used to identify key 12 persons, events, times, places, and objects and narrow down the query's scope. Intent detection may be performed using statistical or rule-based methods. For example, [PERSON] et al. [39] used Naive Bayes and logistic regression machine-learning methods to perform intent detection, with the term frequency-inverse document frequency being the classification feature. In 2012, [PERSON] et al. [40] converted short snippets into vectors to perform intent (sentiment) classification and compared the SVM, Naive Bayes, and continuous bag-of-words methods. The typical representative one of the supervised learning methods is the SVM. Over the past 10 years, many scholars have uses SVM as a comparative method for intent classification or sentiment analysis, and the summary references are as shown in Table 2. In addition, deep-learning models have also become commonplace in intent detection. [PERSON] et al. [41] used a recurrent neural network to perform multi-staged named-entity learning and then used the named entities as classification features. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Interface** & **NLU** & **Dialogue State Tracking** & NLG \\ \hline Radiobot-CFF [35] & Spoken & \(\blacktriangledown\) & \(\blacktriangledown\) & \(\blacktimes\) \\ \hline [PERSON] [36] & Text & \(\blacktriangledown\) & \(\blacktriangledown\) & \(\blacktimes\) \\ \hline MIRIAM [21] & Multimodal & \(\blacktriangledown\) & \(\blacktriangledown\) & \(\blacktriangledown\) \\ \hline [PERSON] [20] & Spoken & \(\blacktriangledown\) & \(\blacktriangledown\) & \(\blacktriangledown\) \\ \hline Our study & Text & \(\blacktriangledown\) & \(\blacktriangledown\) & \(\blacktriangledown\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of military conversational systems. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **CA 1** & **Domain** & **Language** & **Data Size** & **Models 2** & **Optimal** \\ \hline [PERSON], 2012 [42] & \(\blacktriangledown\) & Community Question Answering & English & 1.5 K &
\begin{tabular}{c} SVM, C4.5, RF, \\ NB, KNN \\ \end{tabular} & SVM \\ \hline [PERSON], 2013 [43] & \(\blacktriangledown\) & Audiovisual Media & English & 27.5 K & SVM, HMM, CRF & SVM \\ \hline [PERSON], 2016 [44] & \(\blacktriangledown\) & Personal Assistant & English & 400 K & SVM & SVM \\ \hline [PERSON], 2016 [45] & \(\bigtimes\) & Sentiment Analysis & English & 8 K & SVM, NB, KNN & SVM \\ \hline [PERSON], 2018 [46] & \(\bigtimes\) & Booking flights/ Accommodation & English & 8 K & SVM, CNN & \(\approx\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of SVM Models used in NLU Applications. Slot filling is another critical task in dialogue systems, as it provides semantic information and helps the conversation system determine which bits of information in a sentence should be searched for.