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Computer Science

An Overview of Chatbot Technology

Profile image of Lefteris MoussiadesLefteris Moussiades

IFIP Advances in Information and Communication Technology

https://doi.org/10.1007/978-3-030-49186-4_31
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11 pages

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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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References (54)

  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
  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
  3. chatbot | Definition of chatbot in English by Lexico Dictionaries. https://www.lexico.com/en/ definition/chatbot
  4. Abu Shawar, B.A., Atwell, E.S.: Chatbots: are they really useful? J. Lang. Technol. Comput. Linguist. 22, 29-49 (2007)
  5. Klopfenstein, L., Delpriori, S., Malatini, S., Bogliolo, A.: The rise of bots: a survey of con- versational interfaces, patterns, and paradigms. In: Proceedings of the 2017 Conference on Designing Interactive Systems, pp. 555-565. Association for Computing Machinery (2017)
  6. Turing, A.M.: Computing machinery and intelligence. Mind 59, 433-460 (1950). https://doi. org/10.1093/mind/LIX.236.433
  7. Weizenbaum, J.: ELIZA-a computer program for the study of natural language communi- cation between man and machine. Commun. ACM 9, 36-45 (1966). https://doi.org/10.1145/ 365153.365168
  8. 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
  9. 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
  10. 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
  11. 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
  12. Molnár, G., Zoltán, S.: The role of chatbots in formal education. Presented at the 15 September 2018
  13. Siri. https://www.apple.com/siri/
  14. What exactly is Alexa? Where does she come from? And how does she work? https://www. digitaltrends.com/home/what-is-amazons-alexa-and-what-can-it-do/
  15. Google Assistant, your own personal Google. https://assistant.google.com/
  16. IBM Watson. https://www.ibm.com/watson
  17. Scopus -Document search. https://www.scopus.com/search/form.uri?display=basic
  18. 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
  19. Ranoliya, B.R., Raghuwanshi, N., Singh, S.: Chatbot for university related FAQs. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, pp. 1525-1530 (2017)
  20. 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
  21. 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)
  22. 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
  23. Go, E., Sundar, S.S.: Humanizing chatbots: the effects of visual, identity and conversational cues on humanness perceptions. Comput. Hum. Behav. 97, 304-316 (2019). https://doi.org/ 10.1016/j.chb.2019.01.020
  24. 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)
  25. Fernandes, A.: NLP, NLU, NLG and how Chatbots work. https://chatbotslife.com/nlp-nlu- nlg-and-how-chatbots-work-dd7861dfc9df
  26. Dale, R.: The return of the chatbots. Nat. Lang. Eng. 22, 811-817 (2016). https://doi.org/10. 1017/S1351324916000243
  27. 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
  28. 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
  29. An e-business chatbot using AIML and LSA -Semantic Scholar. https://www.semanticscho lar.org/paper/An-e-business-chatbot-using-AIML-and-LSA-Thomas/906c91ca389a29b47a 0ec072d54e23ddaa757c88
  30. Artificial Intelligence Scripting Language -RiveScript.com. https://www.rivescript.com/
  31. 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
  32. Canonico, M., Russis, L.D.: A comparison and critique of natural language understanding tools. Presented at the (2018)
  33. Nimavat, K., Champaneria, T.: Chatbots: an overview types, architecture, tools and future possibilities. Int. J. Sci. Res. Dev. 5, 1019-1024 (2017)
  34. 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
  35. Hien, H.T., Cuong, P.-N., Nam, L.N.H., Nhung, H.L.T.K., Thang, L.D.: Intelligent assis- tants in higher-education environments: the FIT-EBot, a chatbot for administrative and learn- ing support. In: Proceedings of the Ninth International Symposium on Information and Communication Technology, pp. 69-76. ACM, New York (2018)
  36. 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)
  37. 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
  38. Zumstein, D., Hundertmark, S.: Chatbots -an interactive technology for personalized communication, transactions and services. IADIS Int. J. WWW/Internet 15, 96-109 (2017)
  39. Fern, A., et al.: The Best Open Source Chatbot Platforms in 2019 (2019). https://blog.ver loop.io/the-best-open-source-chatbot-platforms-in-2019/
  40. 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)
  41. Nayyar, D.A.: Chatbots and the Open Source Tools You Can Use to Develop Them (2019). https://opensourceforu.com/2019/01/chatbots-and-the-open-source-tools-you- can-use-to-develop-them/
  42. Dialogflow. https://dialogflow.com/
  43. LUIS (Language Understanding) -Cognitive Services -Microsoft Azure. https://www.luis. ai/home
  44. Amazon Lex -Build Conversation Bots. https://aws.amazon.com/lex/
  45. SAP Conversational AI | Automate Customer Service With AI Chatbots. https://cai.tools.sap
  46. Rasa: Open source conversational AI. https://rasa.com/
  47. Botsify: Botsify -Create Automated Chatbots Online for Facebook Messenger or Website. https://botsify.com
  48. Chatfuel. https://chatfuel.com/
  49. ManyChat -Chat Marketing Made Easy. https://manychat.com/
  50. AI Online Chatbot Software, Live Chat on Websites. https://flowxo.com/
  51. About ChatterBot -ChatterBot 1.0.2 documentation. https://chatterbot.readthedocs.io/en/ stable/
  52. Pandorabots: Home. https://home.pandorabots.com/home.html
  53. Botkit: Building Blocks for Building Bots. https://botkit.ai/
  54. Botlytics. https://www.botlytics.co/

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