Tourism Information Multi-domain Dialogue State Tracking Datasets for Smart Tourism Chatbot



Dialogue State Tracking, Smart Tourism Chatbot, Task-oriented Dialog System


The smart tourism service provides tourism information and recommended travel products so that tourists can create a personalized travel itinerary before travel and provides a tour guide service according to the travel itinerary during travel. To efficiently provide smart tourism services to tourists, it is necessary to develop a chatbot system as well as a smart tourism web/app. The smart tourism chatbot system consists of tourism information Named Entity Recognition (NER), Dialogue State Tracking (DST), and Question and Answering (QA) models. In this paper, we develop the tourism information multi-domain DST datasets to provide chatbot services using the tourism information knowledge base of the smart tourism information system implemented with Neo4J DB. The developed tourism information multi-domain DST datasets consist of 5 domains and 22 slots. The joint goal accuracy of the SOM-DST model [8] using the developed tourism information multi-domain DST dataset is 0.622.


Download data is not yet available.


JeongWoo Jwa, “Development of Personalized Travel Products for Smart Tour Guidance Services”, International Journal of Engineering & Technology, 7 (3.33) 58-61, 2018.

Dong-Hyun Kim, Hyeon-Su Im, Jong-Heon Hyeon, Jeong-Woo Jwa, “Development of the Rule-based Smart Tourism Chatbot using Neo4J graph database”, International Journal of Internet, Broadcasting and Communication, Vol.13, No.2, pp 179-186, 2021.

Garg, D. K. . (2022). Understanding the Purpose of Object Detection, Models to Detect Objects, Application Use and Benefits. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 01–04.

Myeong-Cheol Jwa, Jeong-Woo Jwa, “Development of Tourism Information Named Entity Recognition Datasets for the Fine-tune KoBERT-CRF Model”, International Journal of Internet, Broadcasting and Communication, Vol.14, No.2, pp 55-62, 2022.

Tume-Bruce, B. A. A. ., A. . Delgado, and E. L. . Huamaní. “Implementation of a Web System for the Improvement in Sales and in the Application of Digital Marketing in the Company Selcom”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 5, May 2022, pp. 48-59, doi:10.17762/ijritcc.v10i5.5553.

Budzianowski, Pawel, et al. "MultiWOZ-A LargeScale Multi-Domain Wizard-of-Oz Dataset for TaskOriented Dialogue Modelling." Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018.

Eric, Mihail, et al. “MultiWOZ 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines. Proceedings of The 12th Language Resources and Evaluation Conference, 2020.

Park, Sungjoon, Jihyung Moon, Sungdong Kim, Won Ik Cho, Jiyoon Han, Jangwon Park, Chisung Song et al., “KLUE: Korean Language Understanding Evaluation.”, arXiv, 2021.

Mahmoud, E. H., Gadelrab, M. S., ElSayed, K., & Sallam, A. R. (2022). Modelling Multilayer Communication Channel in Terahertz Band for Medical Applications. International Journal of Communication Networks and Information Security (IJCNIS), 13(3).

Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, Xuanjing Huang, “Pre-trained Models for Natural Language Processing: A Survey”, Science China Technological Sciences 63(10), pp.1872-1897, 2020.

Kim, Sungdong, et al. "Efficient dialogue state tracking by selectively overwriting memory." Proceedings of the 58th Annual Meetings of the Association for Computational Linguistics, 2020.

The proposedsmart tourismsystemthat provides chatbot services using the tourism information knowledge base




How to Cite

M.-C. . Jwa, T.-S. . Ko, B.-J. . Kim, and J.-W. . Jwa, “Tourism Information Multi-domain Dialogue State Tracking Datasets for Smart Tourism Chatbot”, Int J Intell Syst Appl Eng, vol. 10, no. 1s, pp. 192 –, Oct. 2022.