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Data Science and Conversational Interfaces: A New Revolution in Digital Business

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Abstract

Recent advances in Artificial Intelligence, Semantic Web and intelligent interaction devices have made conversational interfaces increasingly popular. These advances in technologies including automatic speech recognition and synthesis, natural language understanding and generation, and dialog management are result of decades of work in these areas to make possible a more natural and intuitive communication with machines. In this chapter, we describe the tremendous potential of Data Science to improve the performance of conversational interfaces and increase the number of users of these interfaces. Following this cycle, the more people use these systems, more data is generated to learn their models and improve their performance, thus increasing the number of users and extending the possibilities for new applications in Digital Business.

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Notes

  1. 1.

    Business Insider: 80% of businesses want chatbots by 2020. http://www.businessinsider.com/80-of-businesses-want-chatbots-by-2020-2016-12 Last accessed: 01/12/2017.).

  2. 2.

    https://dev.botframework.com/.

  3. 3.

    https://www.luis.ai/.

  4. 4.

    https://www.ibm.com/watson/.

  5. 5.

    https://developer.amazon.com/alexa.

  6. 6.

    https://developers.google.com/actions/.

  7. 7.

    https://developers.facebook.com/products/messenger/.

  8. 8.

    https://api.ai/.

  9. 9.

    https://wit.ai/.

References

  1. Baker, J., Deng, L., Glass, J., Khudanpur, S., Lee, C., Morgan, N., et al. (2009). Developments and directions in speech recognition and understanding. IEEE Signal Processing Magazine, 26(3), 75–80.

    Article  Google Scholar 

  2. Bohus, D., Grau, S., Huggins-Daines, D., Keri, V., Krishna, G., Kumar, R., et al. (2007) Conquest—an Open-Source Dialog System for Conferences. In Proceedings of of Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Rochester, NY, USA (pp. 9–12).

    Google Scholar 

  3. Cavazza, M., de la Cámara, & R.S., Turunen. (2010). How Was Your Day? a Companion ECA. In Proceedings of International Conference on Autonomous Agents and Multiagent Systems (AAMAS’10), Toronto, Canada (pp. 1629–1630).

    Google Scholar 

  4. Ezen-Can, A., & Boyer, K. (2013). Unsupervised classification of student dialogue acts with query-likelihood clustering. In Proceedings of 6th International Conference on Educational Data Mining (pp. 2–9).

    Google Scholar 

  5. Fryer, L., & Carpenter, R. (2006). Bots as Language Learning Tools. Language Learning and Technology, 10(3), 8–14.

    Google Scholar 

  6. García-Márquez, F., & Lev, B. (2017). Big data management. Springer International Publishing.

    Google Scholar 

  7. Glass, J., Flammia, G., Goodine, D., Phillips, M., Polifroni, J., Sakai, S., et al. (1995). Multilingual spoken-language understanding in the MIT Voyager system. Speech Communication, 17, 1–18.

    Article  Google Scholar 

  8. Griol, D., Callejas, Z., López-Cózar, R., & Riccardi, G. (2014). A domain-independent statistical methodology for dialog management in spoken dialog systems. Computer Speech & Language, 28(3), 743–768.

    Article  Google Scholar 

  9. Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., & Jaitly, N. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 82, 82–97.

    Article  Google Scholar 

  10. Hodson, H. (2014). The first family robot. New Scientist, 223(2978), 21–22.

    Article  Google Scholar 

  11. Lee, G., Kim, H., Jeong, M., & Kim, J. (2015). Natural language dialog systems and intelligent assistants. Springer.

    Google Scholar 

  12. Levin, E., Pieraccini, R., & Eckert, W. (2000). A stochastic model of human-machine interaction for learning dialog strategies. IEEE Transactions on Speech and Audio Processing, 8(1), 11–23.

    Article  Google Scholar 

  13. McTear, M. F. (2017). Future and emerging trends in language technology. In Second International Workshop on Machine Learning and Big Data, FETLT 2016, Chap. The Rise of the Conversational Interface: A New Kid on the Block? (pp. 38–49). Springer International Publishing.

    Google Scholar 

  14. McTear, M. F., Callejas, Z., & Griol, D. (2016). The conversational interface: Talking to smart devices. Springer.

    Google Scholar 

  15. Melin, H., Sandell, A., & Ihse, M. (2001). CTT-bank: A speech controlled telephone banking system—An initial evaluation. In TMH Quarterly Progress and Status Report (TMH-QPSR) (Vol. 1, pp. 1–27).

    Google Scholar 

  16. Meng, H. H., Wai, C., & Pieraccini, R. (2003). The use of belief networks for mixed-initiative dialog modeling. IEEE Transactions on Speech and Audio Processing, 11(6), 757–773.

    Article  Google Scholar 

  17. Nigam, K., Lafferty, J., & Mccallum, A. (1999). Using maximum entropy for text classification. In Proceedings of IJCAI-99 Workshop on Machine Learning for Information Filtering (pp. 61–67).

    Google Scholar 

  18. Nigam, K., McCalum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2/3), 103–134. https://citeseer.nj.nec.com/nigam99text.html.

  19. Ota, R., & Kimura, M. (2014). Proposal of open-ended dialog system based on topic maps. Procedia Technology, 17, 122–129.

    Article  Google Scholar 

  20. Pieraccini, R., & Rabiner, L. (2012). The voice in the machine: Building computers that understand speech. The MIT Press.

    Google Scholar 

  21. Pon-Barry, H., Schultz, K., Bratt, E. O., Clark, B., & Peters, S. (2006). Responding to student uncertainty in spoken tutorial dialogue systems. International Journal of Artificial Intelligence in Education, 16, 171–194.

    Google Scholar 

  22. Reiter, E., & Dale, R. (1997). Building applied natural language generation systems. Journal of Natural Language Engineering, 3(1), 57–87.

    Article  Google Scholar 

  23. Schatzmann, J., Weilhammer, K., Stuttle, M., & Young, S. (2006). A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies. Knowledge Engineering Review, 21(2), 97–126.

    Article  Google Scholar 

  24. Scheffler, K., & Young, S. 2001. Corpus-based dialogue simulation for automatic strategy learning and evaluation. In Proceedings of the 2nd Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL-2001). Workshop on Adaptation in Dialogue Systems, Pittsburgh, USA (pp. 64–70).

    Google Scholar 

  25. Suendermann, D., & Pieraccini, R. (2012). One year of contender: What have we learned about assessing and tuning industrial spoken dialog systems? In Proceedings of SD-CTD’12 (pp. 45–48).

    Google Scholar 

  26. Vaquero, C., Saz, O., Lleida, E., Marcos, J., & Canalís, C. (2006). VOCALIZA: An application for computer-aided speech therapy in Spanish language. In Proceedings of IV Jornadas en Tecnología del Habla, Zaragoza, Spain (pp. 321–326).

    Google Scholar 

  27. Wang, X., & Yuan, C. (2016). Recent advances on human-computer dialogues. CAAI Transactions on Intelligence Technology, 1(4), 303–312.

    Article  Google Scholar 

  28. Wang, Y., Wang, W., & Huang, C. (2007). Enhanced semantic question answering system for e-learning environment. In Proceedings of of 21st Conference on Advanced Information Networking and Applications (AINAW’07), Niagara Falls, Canada (pp. 1023–1028).

    Google Scholar 

  29. Watkins, C., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292.

    Google Scholar 

  30. Williams, J., & Young, S. (2007). Partially observable markov decision processes for spoken dialog systems. Computer Speech and Language, 21(2), 393–422.

    Article  Google Scholar 

  31. Young, S., Gasic, M., Thomson, B., & Williams, J. (2013). POMDP-based statistical spoken dialogue systems: A review. Proceedings of IEEE, 101(5), 1160–1179.

    Article  Google Scholar 

  32. Young, S., Schatzmann, J., Weilhammer, K., & Ye, H.: The hidden information state approach to dialogue management. In Proceedinds of 32nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Haway, USA (Vol. 4, pp. 149–152).

    Google Scholar 

  33. Zue, V., Seneff, S., Glass, J., Polifroni, J., Pao, C., Hazen, T., et al. (2000). JUPITER: A telephone-based conversational interface for weather information. IEEE Transactions on Speech and Audio Processing, 8(1), 85–96.

    Article  Google Scholar 

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Correspondence to David Griol .

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Griol, D., Callejas, Z. (2019). Data Science and Conversational Interfaces: A New Revolution in Digital Business. In: García Márquez, F., Lev, B. (eds) Data Science and Digital Business. Springer, Cham. https://doi.org/10.1007/978-3-319-95651-0_3

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