Abstract
Chatbots are systems that imitate human conversation by using Artificial Intelligence. They have been designed to interact with users by using natural language in a way that they think they are having a dialogue with a human. The relevance of these is gaining impact in our society, being widely applied to numerous fields, from Health Care to Education. Although their usage is associated with different purposes such as virtual assistant, entertainment, domotic, and routing, they are becoming increasingly popular in business domains, managing the customer services since they can automate, optimize and manage business processes and marketing campaigns. However, it is an arduous task to integrate them into the business data flows to take advantage of their potential and stand out from the competence. Therefore, in this work, we have described IVRMaker, an interactive and customizable telephone chatbot services platform. The target behind the IVRMaker is to help companies to integrate a conversational assistant into their business process. The platform is mainly based on cutting-edge research areas in Natural Language Processing to facilitate easy integration into the business data model. The evaluation of the platform was carried out in two use cases relating to disparate domains. The results obtained were interesting in demonstrating the applicability and adaptability of these assistants and their direct impact on the automatization of customer services and marketing campaigns.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adamopoulou, E., Moussiades, L.: An overview of chatbot technology. In: Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2020. IAICT, vol. 584, pp. 373–383. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49186-4_31
Clarizia, F., Colace, F., Lombardi, M., Pascale, F., Santaniello, D.: Chatbot: an education support system for student. In: Castiglione, A., Pop, F., Ficco, M., Palmieri, F. (eds.) CSS 2018. LNCS, vol. 11161, pp. 291–302. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01689-0_23
Comendador, B.E.V., Francisco, B.M.B., Medenilla, J.S., Mae, S.: Pharmabot: a pediatric generic medicine consultant chatbot. J. Autom. Control Eng. 3(2) (2015)
García-Sánchez, F., Valencia-García, R., Martínez-Béjar, R.: An integrated approach for developing e-commerce applications. Expert Syst. Appl. 28(2), 223–235 (2005). https://doi.org/10.1016/j.eswa.2004.10.004
Hashimoto, K., Xiong, C., Tsuruoka, Y., Socher, R.: A joint many-task model: Growing a neural network for multiple NLP tasks. arXiv preprint arXiv:1611.01587 (2016)
Kaimakis, N.J., Davis, D., Breck, S., Nye, B.: Domain-specific reduction of language model databases: Overcoming chatbot implementation obstacles. In: the Proceedings of the MODSIM World Conference, Norfolk, Virginia (2018)
Kushwaha, A.K., Kar, A.K.: Language model-driven chatbot for business to address marketing and selection of products. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Rana, N.P. (eds.) TDIT 2020. IAICT, vol. 617, pp. 16–28. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64849-7_3
Li, F.L., et al.: AliMe assist: an intelligent assistant for creating an innovative e-commerce experience. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2495–2498 (2017)
May, C., Wang, A., Bordia, S., Bowman, S.R., Rudinger, R.: On measuring social biases in sentence encoders. arXiv preprint arXiv:1903.10561 (2019)
Molnár, G., Szüts, Z.: The role of chatbots in formal education. In: 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY), pp. 000197–000202. IEEE (2018)
Parikh, A.P., Täckström, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933 (2016)
qizi Qodirova, D.B.: Analysis of changes in the semantic structure and lexical and semantic relations of English words in Uzbek. In: International Conferences, vol. 1, pp. 3–7 (2022)
Ranoliya, B.R., Raghuwanshi, N., Singh, S.: Chatbot for university related FAQs. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1525–1530. IEEE (2017)
Ruder12, S., Bingel, J., Augenstein, I., Søgaard, A.: Learning what to share between loosely related tasks. arXiv preprint arXiv:1705.08142 (2017)
Ruiz-Sánchez, J.M., Valencia-García, R., Fernández-Breis, J.T., Martínez-Béjar, R., Compton, P.: An approach for incremental knowledge acquisition from text. Expert Syst. Appl. 25(1), 77–86 (2003). https://doi.org/10.1016/S0957-4174(03)00008-3
Seo, M., Kembhavi, A., Farhadi, A., Hajishirzi, H.: Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603 (2016)
Sha, L., Chang, B., Sui, Z., Li, S.: Reading and thinking: Re-read LSTM unit for textual entailment recognition. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2870–2879 (2016)
Stern, M., Andreas, J., Klein, D.: A minimal span-based neural constituency parser. arXiv preprint arXiv:1705.03919 (2017)
Suhaili, S.M., Salim, N., Jambli, M.N.: Service chatbots: a systematic review. Expert Syst. Appl. 184, 115461 (2021)
Valencia-García, R., Ruiz-Sánchez, J.M., Vicente, P.J.V., Fernández-Breis, J.T., Martínez-Béjar, R.: An incremental approach for discovering medical knowledge from texts. Expert Syst. Appl. 26(3), 291–299 (2004). https://doi.org/10.1016/j.eswa.2003.09.001
Wieting, J., Kiela, D.: No training required: Exploring random encoders for sentence classification. arXiv preprint arXiv:1901.10444 (2019)
Acknowledgements
This work has been funded by INFO and the European Regional Development Fund (FEDER/ERDF) under the RIS3MUR COVID-19 program through project IVRMAKER (2020.08.ID+I.0020). This work has been also partially supported by the projects “Programa para la Recualificación del Sistema Universitario Español 2021–2023”, and the Community of Madrid, through the Young Researchers R+D Project. Ref. M2173 - SGTRS (co-funded by Rey Juan Carlos University) and PEJD-2019-PRE/TIC-16151.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rodríguez-García, M.Á., Caparrós-Laiz, C., Vivancos-Vicente, P.J., García-Díaz, J.A., Valencia-García, R. (2022). IVRMaker, An Interactive and Customizable Telephone Chatbot Services Platform. In: Valencia-García, R., Bucaram-Leverone, M., Del Cioppo-Morstadt, J., Vera-Lucio, N., Jácome-Murillo, E. (eds) Technologies and Innovation. CITI 2022. Communications in Computer and Information Science, vol 1658. Springer, Cham. https://doi.org/10.1007/978-3-031-19961-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-19961-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19960-8
Online ISBN: 978-3-031-19961-5
eBook Packages: Computer ScienceComputer Science (R0)