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IVRMaker, An Interactive and Customizable Telephone Chatbot Services Platform

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Technologies and Innovation (CITI 2022)

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.

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Notes

  1. 1.

    https://matplotlib.org/.

  2. 2.

    https://flask.palletsprojects.com/en/2.1.x/.

  3. 3.

    https://www.asterisk.org/.

  4. 4.

    https://kaldi-asr.org/.

  5. 5.

    https://core.telegram.org/.

  6. 6.

    https://spacy.io.

  7. 7.

    https://www.bootpress.org/.

  8. 8.

    https://www.nltk.org/.

References

  1. 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

    Chapter  Google Scholar 

  2. 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

    Chapter  Google Scholar 

  3. 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)

    Google Scholar 

  4. 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

    Article  Google Scholar 

  5. 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)

  6. 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)

    Google Scholar 

  7. 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

    Chapter  Google Scholar 

  8. 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)

    Google Scholar 

  9. May, C., Wang, A., Bordia, S., Bowman, S.R., Rudinger, R.: On measuring social biases in sentence encoders. arXiv preprint arXiv:1903.10561 (2019)

  10. 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)

    Google Scholar 

  11. Parikh, A.P., Täckström, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933 (2016)

  12. 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)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. Ruder12, S., Bingel, J., Augenstein, I., Søgaard, A.: Learning what to share between loosely related tasks. arXiv preprint arXiv:1705.08142 (2017)

  15. 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

    Article  Google Scholar 

  16. Seo, M., Kembhavi, A., Farhadi, A., Hajishirzi, H.: Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603 (2016)

  17. 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)

    Google Scholar 

  18. Stern, M., Andreas, J., Klein, D.: A minimal span-based neural constituency parser. arXiv preprint arXiv:1705.03919 (2017)

  19. Suhaili, S.M., Salim, N., Jambli, M.N.: Service chatbots: a systematic review. Expert Syst. Appl. 184, 115461 (2021)

    Article  Google Scholar 

  20. 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

    Article  Google Scholar 

  21. Wieting, J., Kiela, D.: No training required: Exploring random encoders for sentence classification. arXiv preprint arXiv:1901.10444 (2019)

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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.

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Correspondence to Miguel Ángel Rodríguez-García .

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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

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  • DOI: https://doi.org/10.1007/978-3-031-19961-5_5

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