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An Overview of Chatbot Technology

Open access
Adamopoulou, Eleni, Moussiades, Lefteris·2020·IFIP Advances in Information and Communication Technology

Abstract

The use of chatbots evolved rapidly in numerous fields in recent years, including Marketing, Supporting Systems, Education, Health Care, Cultural Heritage, and Entertainment. In this paper, we first present a historical overview of the evolution of the international community’s interest in chatbots. Next, we discuss the motivations that drive the use of chatbots, and we clarify chatbots’ usefulness in a variety of areas. Moreover, we highlight the impact of social stereotypes on chatbots design. After clarifying necessary technological concepts, we move on to a chatbot classification based on various criteria, such as the area of knowledge they refer to, the need they serve and others. Furthermore, we present the general architecture of modern chatbots while also mentioning the main platforms for their creation. Our engagement with the subject so far, reassures us of the prospects of chatbots and encourages us to study them in greater extent and depth.

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Cites

Research works printed but not linked: 26

  1. Bansal, H., Khan, R.: A review paper on human computer interaction. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 8, 53 (2018). https://doi.org/10.23956/ijarcsse.v8i4.630 · DOI
  2. Khanna, A., Pandey, B., Vashishta, K., Kalia, K., Bhale, P., Das, T.: A study of today’s A.I. through chatbots and rediscovery of machine intelligence. Int. J. u- e-Serv. Sci. Technol. 8, 277–284 (2015). https://doi.org/10.14257/ijunesst.2015.8.7.28 · DOI · Paper page
  3. Abu Shawar, B.A., Atwell, E.S.: Chatbots: are they really useful? J. Lang. Technol. Comput. Linguist. 22, 29–49 (2007) · DOI
  4. Klopfenstein, L., Delpriori, S., Malatini, S., Bogliolo, A.: The rise of bots: a survey of conversational interfaces, patterns, and paradigms. In: Proceedings of the 2017 Conference on Designing Interactive Systems, pp. 555–565. Association for Computing Machinery (2017) · DOI
  5. Brandtzaeg, P.B., Følstad, A.: Why people use chatbots. In: Kompatsiaris, I., et al. (eds.) Internet Science, pp. 377–392. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70284-1_30 · DOI · Paper page
  6. Colby, K.M., Weber, S., Hilf, F.D.: Artificial paranoia. Artif. Intell. 2, 1–25 (1971). https://doi.org/10.1016/0004-3702(71)90002-6 · DOI
  7. Wallace, R.S.: The anatomy of A.L.I.C.E. In: Epstein, R., Roberts, G., Beber, G. (eds.) Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer, pp. 181–210. Springer, Cham (2009). https://doi.org/10.1007/978-1-4020-6710-5_13 · DOI
  8. Marietto, M., et al.: Artificial intelligence markup language: a brief tutorial. Int. J. Comput. Sci. Eng. Surv. 4 (2013). https://doi.org/10.5121/ijcses.2013.4301 · DOI
  9. Molnár, G., Zoltán, S.: The role of chatbots in formal education. Presented at the 15 September 2018 · DOI
  10. Colace, F., De Santo, M., Lombardi, M., Pascale, F., Pietrosanto, A., Lemma, S.: Chatbot for e-learning: a case of study. Int. J. Mech. Eng. Robot. Res. 7, 528–533 (2018). https://doi.org/10.18178/ijmerr.7.5.528-533 · DOI
  11. da Costa, P.C.F.: Conversing with personal digital assistants: on gender and artificial intelligence. J. Sci. Technol. Arts 10, 59–72 (2018). https://doi.org/10.7559/citarj.v10i3.563 · DOI
  12. Xu, A., Liu, Z., Guo, Y., Sinha, V., Akkiraju, R.: A new chatbot for customer service on social media. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 3506–3510. ACM, New York (2017) · DOI
  13. Følstad, A., Nordheim, C.B., Bjørkli, C.A.: What makes users trust a chatbot for customer service? An exploratory interview study. In: Bodrunova, S.S. (ed.) INSCI 2018. LNCS, vol. 11193, pp. 194–208. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01437-7_16 · DOI
  14. Sannon, S., Stoll, B., DiFranzo, D., Jung, M., Bazarova, N.N.: How personification and interactivity influence stress-related disclosures to conversational agents. In: Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 285–288. ACM, New York (2018) · DOI
  15. Dale, R.: The return of the chatbots. Nat. Lang. Eng. 22, 811–817 (2016). https://doi.org/10.1017/S1351324916000243 · DOI · Paper page
  16. Ramesh, K., Ravishankaran, S., Joshi, A., Chandrasekaran, K.: A survey of design techniques for conversational agents. In: Kaushik, S., Gupta, D., Kharb, L., Chahal, D. (eds.) ICICCT 2017. CCIS, vol. 750, pp. 336–350. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6544-6_31 · DOI
  17. Akma, N., Hafiz, M., Zainal, A., Fairuz, M., Adnan, Z.: Review of chatbots design techniques. Int. J. Comput. Appl. 181, 7–10 (2018). https://doi.org/10.5120/ijca2018917606 · DOI
  18. An e-business chatbot using AIML and LSA - Semantic Scholar. https://www.semanticscholar.org/paper/An-e-business-chatbot-using-AIML-and-LSA-Thomas/906c91ca389a29b47a0ec072d54e23ddaa757c88
  19. Jung, S.: Semantic vector learning for natural language understanding. Comput. Speech Lang. 56, 130–145 (2019). https://doi.org/10.1016/j.csl.2018.12.008 · DOI
  20. Nimavat, K., Champaneria, T.: Chatbots: an overview types, architecture, tools and future possibilities. Int. J. Sci. Res. Dev. 5, 1019–1024 (2017)
  21. Kucherbaev, P., Bozzon, A., Houben, G.-J.: Human-aided bots. IEEE Internet Comput. 22, 36–43 (2018). https://doi.org/10.1109/MIC.2018.252095348 · DOI
  22. Hien, H.T., Cuong, P.-N., Nam, L.N.H., Nhung, H.L.T.K., Thang, L.D.: Intelligent assistants in higher-education environments: the FIT-EBot, a chatbot for administrative and learning support. In: Proceedings of the Ninth International Symposium on Information and Communication Technology, pp. 69–76. ACM, New York (2018) · DOI
  23. Wu, Y., Wu, W., Xing, C., Zhou, M., Li, Z.: Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots. arXiv:1612.01627 [cs] (2016) · DOI
  24. Augello, A., Gentile, M., Dignum, F.: An overview of open-source chatbots social skills. In: Diplaris, S., Satsiou, A., Følstad, A., Vafopoulos, M., Vilarinho, T. (eds.) INSCI 2017. LNCS, vol. 10750, pp. 236–248. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77547-0_18 · DOI
  25. Zumstein, D., Hundertmark, S.: Chatbots – an interactive technology for personalized communication, transactions and services. IADIS Int. J. WWW/Internet 15, 96–109 (2017)
  26. Singh, S., Darbari, H., Bhattacharjee, K., Verma, S.: Open source NLG systems: a survey with a vision to design a true NLG system. 9, 4409–4421 (2016)

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Metadata

Authors
Adamopoulou, E., & Moussiades, L.
Venue
IFIP Advances in Information and Communication Technology
Published
2020
Pages
373-383
ISSN
  • 1868-422X (ISSN-L)
  • 1868-4238
OpenAlex
W3028999579
Cited by
990
References
4
FWCI
42.43
Type
conference paper
Language
en
Open access
green
Licence

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