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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 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.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
References
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.
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).
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).
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).
Fryer, L., & Carpenter, R. (2006). Bots as Language Learning Tools. Language Learning and Technology, 10(3), 8–14.
García-Márquez, F., & Lev, B. (2017). Big data management. Springer International Publishing.
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.
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.
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.
Hodson, H. (2014). The first family robot. New Scientist, 223(2978), 21–22.
Lee, G., Kim, H., Jeong, M., & Kim, J. (2015). Natural language dialog systems and intelligent assistants. Springer.
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.
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.
McTear, M. F., Callejas, Z., & Griol, D. (2016). The conversational interface: Talking to smart devices. Springer.
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).
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.
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).
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.
Ota, R., & Kimura, M. (2014). Proposal of open-ended dialog system based on topic maps. Procedia Technology, 17, 122–129.
Pieraccini, R., & Rabiner, L. (2012). The voice in the machine: Building computers that understand speech. The MIT Press.
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.
Reiter, E., & Dale, R. (1997). Building applied natural language generation systems. Journal of Natural Language Engineering, 3(1), 57–87.
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.
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).
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).
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).
Wang, X., & Yuan, C. (2016). Recent advances on human-computer dialogues. CAAI Transactions on Intelligence Technology, 1(4), 303–312.
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).
Watkins, C., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292.
Williams, J., & Young, S. (2007). Partially observable markov decision processes for spoken dialog systems. Computer Speech and Language, 21(2), 393–422.
Young, S., Gasic, M., Thomson, B., & Williams, J. (2013). POMDP-based statistical spoken dialogue systems: A review. Proceedings of IEEE, 101(5), 1160–1179.
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).
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-95651-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95650-3
Online ISBN: 978-3-319-95651-0
eBook Packages: Business and ManagementBusiness and Management (R0)