Conversational AI over Military Scenarios Using Intent Detection and Response Generation

Chuang, Hsiu-Min; Cheng, Ding-Wei

doi:10.3390/app12052494

Cited by 15Speech and dialogue systemsAI in Service InteractionsNatural Language Processing Techniques

Conversational AI over Military Scenarios Using Intent Detection and Response Generation

Open access

Chuang, Hsiu-Min, Cheng, Ding-Wei·2022·Applied Sciences

DOI 10.3390/app12052494

Open this paper in CoChat — free account Open PDF

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 conversational agents over military scenarios. Additionally, the proposed conversational system can be trained by other datasets for future application domains.

Integrity signals

Registry

Crossref · 10.3390/app12052494

Cites

57 of 57 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

By deploying a conversational military agent, soldiers will get prompt, pertinent responses while working on repetitious chores with less effort and expense.
“Additionally, by deploying a conversational military agent, soldiers will get prompt, pertinent responses while working on repetitious chores with less efort and expense [41].”
From Artificial Intelligence in the Military: An Overview of the Capabilities, Applications, and Challenges (2023) · cites this paper as [41]

Support-vector networks
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992 · see citing works
Introduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288 · see citing works
A comparison of methods for multiclass support vector machines
Hsu, C., & Lin, C. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,861 · see citing works
ELIZA—a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1966) · Communications of the ACM · doi:10.1145/365153.365168 · Cited by 4,452 · see citing works
Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017) · JMIR Mental Health · doi:10.2196/mental.7785 · Cited by 2,730 · see citing works
Learning to Rank for Information Retrieval
Liu, T. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000016 · Cited by 1,575 · see citing works
Baselines and Bigrams: Simple, Good Sentiment and Topic Classification
Wang, S., & Manning, C. D. (2012) · Cited by 957 · see citing works
The ATIS spoken language systems pilot corpus
Hemphill, C. T., Godfrey, J. J., & Doddington, G. R. (1990) · DARPA Workshop on Speech and Natural Language (HLT) · doi:10.3115/116580.116613 · Cited by 810 · see citing works
A Survey on Dialogue Systems: Recent Advances and New Frontiers
Chen, H., Liu, X., Yin, D., & Tang, J. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3166054.3166058 · Cited by 579 · see citing works
Neural Belief Tracker: Data-Driven Dialogue State Tracking
Mrkšić, N., Séaghdha, D. Ó., Wen, T.-H., Thomson, B., & Young, S. J. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1163 · Cited by 460 · see citing works
Towards Conversational Search and Recommendation: System Ask, User Respond
Zhang, Y., Chen, X., Ai, Q., Yang, L., & Croft, W. B. (2018) · Proceedings of the 27th ACM International Conference on Information and Knowledge Management · doi:10.1145/3269206.3271776 · Cited by 319 · see citing works
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs
Moon, S., Shah, P., Kumar, A., & Subba, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1081 · Cited by 306 · see citing works
A Survey on Conversational Agents/Chatbots Classification and Design Techniques
Hussain, S., Ameri Sianaki, O., & Ababneh, N. (2019) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-030-15035-8_93 · Cited by 279 · see citing works
BIOBIBLIOMETRICS: INFORMATION RETRIEVAL AND VISUALIZATION FROM CO-OCCURRENCES OF GENE NAMES IN MEDLINE ABSTRACTS
Stapley, B. J., & Benoît, G. (1999) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814447331_0050 · Cited by 273 · see citing works
Class Imbalance Learning Methods for Support Vector Machines
Batuwita, R., & Palade, V. (2013) · doi:10.1002/9781118646106.ch5 · Cited by 235 · see citing works
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
Zhao, T., & Eskénazi, M. (2016) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w16-3601 · Cited by 192 · see citing works
MedDialog: Large-scale Medical Dialogue Datasets
Zeng, G., Yang, W., Ju, Z., Yang, Y., Wang, S., Zhang, R., Zhou, M., Zeng, J., Dong, X., Zhang, R., et al. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.743 · Cited by 178 · see citing works
A Survey of Joint Intent Detection and Slot Filling Models in Natural Language Understanding
Weld, H., Huang, X., Long, S., Poon, J., & Han, S. C. (2022) · ACM Computing Surveys · doi:10.1145/3547138 · Cited by 172 · see citing works
A Survey of Design Techniques for Conversational Agents
Ramesh, K., Ravishankaran, S., Joshi, A., & Chandrasekaran, K. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-6544-6_31 · Cited by 170 · see citing works
Conversational Agents: Goals, Technologies, Vision and Challenges
Allouch, M., Azaria, A., & Azoulay, R. (2021) · Sensors · doi:10.3390/s21248448 · Cited by 168 · see citing works
Easy contextual intent prediction and slot detection
Bhargava, A., Celikyilmaz, A., Hakkani-Tur, D., & Sarikaya, R. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639291 · Cited by 91 · see citing works
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation
Juraska, J., Karagiannis, P., Bowden, K., & Walker, M. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1014 · Cited by 75 · see citing works
Understanding user intent in community question answering
Chen, L., Zhang, D., & Mark Levene, A. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187980.2188206 · Cited by 70 · see citing works
Performance issues and error analysis in an open-domain Question Answering system
MOLDOVAN, D. I., PAŞCA, M. A., Harabagiu, S. M., & Surdeanu, M. (2001) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1073083.1073091 · Cited by 69 · see citing works
A survey on near-human conversational agents
Singh, S., & Beniwal, H. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.10.013 · Cited by 63 · see citing works
Comparison Of Multinomial Naive Bayes Algorithm And Logistic Regression For Intent Classification In Chatbot
Setyawan, M. Y. H., Awangga, R. M., & Efendi, S. R. (2018) · International Conference on Applied Engineering (ICAE) · doi:10.1109/incae.2018.8579372 · Cited by 63 · see citing works
Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines
Troussas, C., Krouska, A., Sgouropoulou, C., & Voyiatzis, I. (2020) · Entropy · doi:10.3390/e22070735 · Cited by 52 · see citing works
Improving Knowledge-Aware Dialogue Generation via Knowledge Base Question Answering
Wang, J., Liu, J., Bi, W., Liu, X., He, K., Xu, R., & Yang, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6453 · Cited by 43 · see citing works
Dialogue Systems for Intelligent Human Computer Interactions
Merdivan, E., Singh, D., Hanke, S., & Holzinger, A. (2019) · Electronic Notes in Theoretical Computer Science · doi:10.1016/j.entcs.2019.04.010 · Cited by 39 · see citing works
A Retrieval-Based Dialogue System Utilizing Utterance and Context Embeddings
Bartl, A., & Spanakis, G. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.00011 · Cited by 38 · see citing works
Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach
Anwer, S., Waris, A., Sultan, H., Butt, S. I., Zafar, M. H., Sarwar, M., Niazi, I. K., Shafique, M. S., & Pujari, A. N. (2020) · Sensors · doi:10.3390/s20195510 · Cited by 26 · see citing works
An Integrated Authoring Tool for Tactical Questioning Dialogue Systems
Gandhe, S., Whitman, N., Traum, D. R., & Artstein, R. (2009) · Cited by 26 · see citing works
Multiclass mood classification on Twitter using lexicon dictionary and machine learning algorithms
Gaikwad, G., & Joshi, D. (2016) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/inventive.2016.7823247 · Cited by 26 · see citing works
Keep Me in the Loop
Robb, D. A., Garcia, F. J. C., Laskov, A., Liu, X., Patrón, P., & Hastie, H. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3242974 · Cited by 22 · see citing works
Intent Classification for a Management Conversational Assistant
Hefny, A. H., Dafoulas, G. A., & Ismail, M. A. (2020) · International Conference on Computer Engineering & Systems (ICCES) · doi:10.1109/icces51560.2020.9334685 · Cited by 21 · see citing works
Automated question answering : template-based approach
Sneiders, E. (2002) · Cited by 20 · see citing works
Radiobot-CFF: a spoken dialogue system for military training
Roque, A., Leuski, A., Rangarajan, V., Robinson, S. M., Vaswani, A., Narayanan, S. S., & Traum, D. R. (2006) · Interspeech (USB) · doi:10.21437/interspeech.2006-151 · Cited by 20 · see citing works
Integrating a hypernymic proposition interpreter into a semantic processor for biomedical texts.
Marcelo Fiszman, B., Rindflesch, T. C., & Halil Kilicoglu, B. (2003) · Europe PMC (PubMed Central) · Cited by 20 · see citing works
Legalbot: A Deep Learning-Based Conversational Agent in the Legal Domain
John, A. K., Di, L., Robaldo, L., & Guido Boella, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59569-6_32 · Cited by 19 · see citing works
Multitask Learning with Knowledge Base for Joint Intent Detection and Slot Filling
He, T., Xu, X., Wu, Y., Wang, H., & Chen, J. (2021) · Applied Sciences · doi:10.3390/app11114887 · Cited by 18 · see citing works
Question answering system: A heuristic approach
Bhoir, V., & Potey, M. A. (2014) · International Conference on the Applications of Digital Information and Web Technologies (ICADIWT) · doi:10.1109/icadiwt.2014.6814704 · Cited by 16 · see citing works
Increasing Naturalness and Flexibility in Spoken Dialogue Interaction
Marchi, E., Siniscalchi, S. M., Cumani, S., Salerno, V. M., & Li, H. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-9323-9 · Cited by 15 · see citing works
Development of Dialogue Management System for Banking Services
Rustamov, S., Bayramova, A., & Alasgarov, E. (2021) · Applied Sciences · doi:10.3390/app112210995 · Cited by 12 · see citing works
A Survey on Chatbot: Futuristic Conversational Agent for User Interaction
Goel, P., & Ganatra, A. P. (2021) · doi:10.1109/icspc51351.2021.9451763 · Cited by 11 · see citing works
Domain knowledge enriched framework for restricted domain question answering system
Malik, N., Sharan, A., & Biswas, P. (2013) · IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) · doi:10.1109/iccic.2013.6724163 · Cited by 11 · see citing works
Automatic Dialogue System of Marriage Law Based on the Parallel C4.5 Decision Tree
Wang, C., Chen, D., Hu, Y., Ceng, Y., Chen, J., & Li, H. (2020) · IEEE Access · doi:10.1109/access.2020.2972586 · Cited by 9 · see citing works
The Evaluation of User Experience Testing for Retrieval-based Model and Deep Learning Conversational Agent
Leong, P. H., Sing, O., Jaya, Y. B. J., Sam, Y. H., & Weng, C. G. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120429 · Cited by 9 · see citing works
Untitled work
(2019) · Naval War College review · Cited by 5 · see citing works
The Intelligent Agent NLP-based Customer Service System
Huang, C. (2021) · International Conference on Artificial Intelligence in Electronics Engineering (AIEE) · doi:10.1145/3460268.3460275 · Cited by 5 · see citing works
Study on Text-based and Voice-based Dialogue Interfaces for Human-Computer Interactions in a Blocks World
Kim, S., Salter, D., DeLuccia, L., & Tamrakar, A. (2020) · Proceedings of the 8th International Conference on Human-Agent Interaction · doi:10.1145/3406499.3418754 · Cited by 4 · see citing works
Probabilistic-Logic Bots for Efficient Evaluation of Business Rules Using Conversational Interfaces
Bockhorst, J. P., Conathan, D., & Fung, G. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019422 · Cited by 3 · see citing works
Combining Support Vector Machines, Border Revised Rules and Transformation-based Error-driven Learning for Chinese Chunking
Yuan, W., Ling-yu, Z., Ya-xuan, Z., Lu, H., & Ding-yi, F. (2010) · doi:10.1109/aici.2010.87 · Cited by 3 · see citing works
A software agent for social networks using natural language processing techniques
Drašković, D., Gencel, V., Žitnik, S., Bajec, M., & Nikolić, B. (2016) · Telecommunications Forum (TELFOR) · doi:10.1109/telfor.2016.7818921 · Cited by 3 · see citing works
Towards Developing Dialogue Systems with Entertaining Conversations
Trieu, H.-L., Iida, H., Bao, N. P. H., & Nguyen, L.-M. (2017) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0006192105110518 · Cited by 3 · see citing works
Comparing the Effectiveness of Support Vector Machines and Convolutional Neural Networks for Determining User Intent in Conversational Agents
O'Sullivan, K. (2018) · Arrow - TU Dublin (Technological University Dublin) · doi:10.21427/d78b9j · Cited by 2 · see citing works
Graph-based Self-Adaptive Conversational Agent
Zhang, L., Li, W., Bai, Q., & Lai, E. M.-K. (2021) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/ejbq2435 · Cited by 2 · see citing works

Flagged first, then by citations

Sorted by citations

Artificial Intelligence in the Military: An Overview of the Capabilities, Applications, and Challenges
Rashid, A. B., Kausik, A. K., Al Hassan Sunny, A., & Bappy, M. H. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/8676366 · Cited by 143 · see citing works
Current Approaches and Applications in Natural Language Processing
Montejo‐Ráez, A., & Jiménez-Zafra, S. M. (2022) · Applied Sciences · doi:10.3390/app12104859 · Cited by 21 · see citing works
AI in the Higher Military Institutions: Challenges and Perspectives for Military Engineering Training
Chmyr, V., & Bhinder, N. (2023) · Rupkatha Journal on Interdisciplinary Studies in Humanities · doi:10.21659/rupkatha.v15n4.11 · Cited by 19 · see citing works
Productivity of incident management with conversational bots-a review
Iparraguirre-Villanueva, O., Obregon-Palomino, L., Pujay-Iglesias, W., Sierra-Liñan, F., & Cabanillas-Carbonell, M. (2023) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v12.i4.pp1543-1556 · Cited by 9 · see citing works
Assessment of complexities in implementation of conversational AI for the digital transformation of small construction project
Althoey, F., Sajjad, M., Houda, M., & Waqar, A. (2025) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2025.103370 · Cited by 5 · see citing works
GenAI in the Military: Trends and Opportunities
Vasankari, L., & Koski, A. (2025) · Scandinavian Journal of Military Studies · doi:10.31374/sjms.415 · Cited by 5 · see citing works
Conceptualizing Collaborative Team Learning in XR for Medical Education and Training
Hartman, C., Kim, I., & Ryu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-61047-9_3 · Cited by 2 · see citing works
LLM-based wargame scenario generation with domain ontology and ECA rules
Bae, Y., Hwang, S., Lee, K.-H., & Lee, K. (2026) · SIMULATION · doi:10.1177/00375497251415245 · Cited by 1 · see citing works
A Bert-based Joint Model of Intent Recognition and Slot Filling Cross-correlation
Wei, J. (2022) · International Conference on Data Science and Information Technology (DSIT) · doi:10.1109/dsit55514.2022.9943940 · Cited by 1 · see citing works
Opportunities for the Development of Military Cognitive Skills I (Theoretical Approach)
Négyesi, I. (2024) · Scientific Bulletin · doi:10.2478/bsaft-2024-0008
Opportunities for the Development of Military Cognitive Skills II (Practical Approach)
Négyesi, I. (2024) · Scientific Bulletin · doi:10.2478/bsaft-2024-0009
Research on Training Scenario Assisted Design Method Based on Domain-Adapted Language Model
Xie, Z., Xu, H., Yang, C., Zhao, L., Li, J., & Liu, A. (2024) · International Conference on New Trends in Computational Intelligence (NTCI) · doi:10.1109/ntci64025.2024.10776221
Research and Design of a Multilevel Intent Recognition Framework Based on Pretrained Models
He, R., Wang, S., Gao, J., & Wang, W. (2025) · doi:10.1145/3730436.3730467
Intent Classification and Response Generation of Conversational Agents: A Literature Review
Fernandes, B., & Shirude, S. B. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-3652-5_3
CRISIS: Classification of Response Intent via Social Media for Improved Support
Schmidt, J., Romano, A. J., Adesokan, A., & Madria, S. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00367

Building Conversational Agents for Military Training: Towards a Virtual Wingman
van Oijen, J., & Claessen, O. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77772-2_34 · Cited by 1 · see citing works
A dialog architecture for military story capture
Smith, R. W., Manning, B., Rogers, J. G., Adams, B., Abdul, M., & Alvarez, A. (2002) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.3115/1118121.1118144 · Cited by 1 · see citing works
Knowledge-Guided Interpretation and Generation of Task-Oriented Dialogue
Gabaldon, A., Pat Langley, B., Meadows, B., & Selker, T. (2016) · Signals and communication technology · doi:10.1007/978-3-319-21834-2_3
A Survey of Conversational Styles and Systems
Nagpal, R., Fatty, S., & Brizan, D. G. (2024) · Conference on Human System Interactions · doi:10.1109/hsi61632.2024.10613569 · Cited by 1 · see citing works
Natural Language Understanding for Conversational Agents
Xia, C. (2022) · Figshare · doi:10.25417/uic.20253840

Authors: Chuang, H.-M., & Cheng, D.-W.
Affiliations: National Defense University (TW)
Corresponding author: Chuang, Hsiu-Min
ORCID: Chuang, Hsiu-Min
Venue: Applied Sciences
Publisher: MDPI AG
Published: 2022-02-27
Volume: 12
Issue: 5
Pages: 2494
ISSN: 2076-3417 (ISSN-L)
DOI: 10.3390/app12052494
OpenAlex: W4214758403
Cited by: 15
References: 57
Citations by year: 2026: 1 · 2025: 5 · 2024: 4 · 2023: 3 · 2022: 2
FWCI: 1.15
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Applied Sciences (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders: Ministry of Science and Technology (MOST-108-2221-E-606-013-MY2)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords: chatbots, military scenarios, intent classification, slot filling, natural language generation, natural language understanding, Chinese language, military education

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Conversational AI over Military Scenarios Using Intent Detection and Response Generation

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks