Abstract
Enhancements in ways a conversational agent can participate in human-computer dialog tasks, as well as in the ability of such agents in answering questions has been explored in different research areas in the last years. But some limitations are still present in most of the current conversational agents’ models. In this paper we present a new formal language, named Cognitive Conversation Language (CCL), designed to give support to conversational agents. CCL can be used to help such agents in a variety of tasks, including questions understanding and answer generation. Empirical results of the use of CCL are provided based on experiments using the knowledge base of a well-know never-ending learning system, called NELL (Never-Ending Language Learner). Results obtained in the experiments are empirical evidence that CCL can contribute in improving the quality of a question & answer task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
NELL: Never-Ending Language Learning, see more in: http://rtw.ml.cmu.edu/.
- 2.
NELL’ KB is freely available in the website of the Carnegie Mellon University project: http://rtw.ml.cmu.edu.
- 3.
References
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI. vol. 5, p. 3 (2010)
Cassell, J.: Embodied Conversational Agents. MIT Press, Cambridge (2000)
Dali, L., Rusu, D., Fortuna, B., Mladenic, D., Grobelnik, M.: Question answering based on semantic graphs. In: Proceedings of the Workshop on Semantic Search (2009)
David., S.O.: Modern conversational agents. In: Wiesbaden, S.F. (ed.) Technologien für digitale Innovationen, pp. 63–84 (2014)
De Pietro, O., Frontera, G.: Tutorbot: an application aiml-based for web-learning. Adv. Technol. Learn. 2(1), 29–34 (2005)
Fader, A., Zettlemoyer, L., Etzioni, O.: Open question answering over curated and extracted knowledge bases. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1156–1165. ACM (2014)
Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A.A., Lally, A., Murdock, J.W., Nyberg, E., Prager, J., et al.: Building watson: an overview of the deepqa project. AI Mag. 31(3), 59–79 (2010)
Johnston, M., Bangalore, S., Vasireddy, G., Stent, A., Ehlen, P., Walker, M., Whittaker, S., Maloor, P.: Match: an architecture for multimodal dialogue systems. In: Proceedings of the 40th ACL Annual Meeting, pp. 376–383. ACL (2002)
Lally, A., Prager, J.M., McCord, M.C., Boguraev, B.K., Patwardhan, S., Fan, J., Fodor, P., Chu-Carroll, J.: Question analysis: how watson reads a clue. IBM J. Res. Dev. 56(34), 2:1–2:14 (2012)
Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing, vol. 999. MIT Press, Cambridge (1999)
Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Betteridge, J., Carlson, A., Dalvi, B., Gardner, M., Kisiel, B., Krishnamurthy, J., et al.: Never-ending learning. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2302–2310. AAAI Press (2015)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Moschitti, A., Quarteroni, S., Basili, R., Manandhar, S.: Exploiting syntactic and shallow semantic kernels for question answer classification. In: Annual Meeting-Association for Computational Linguistics, vol. 45, p. 776 (2007)
Pappu, A.K., Rudnicky, A.I.: Knowledge acquisition strategies for goal-oriented dialog systems. In: Proceedings of the SIGDIAL Conference (2014)
Pedro, S.D., Appel, A.P., Hruschka Jr., E.R.: Autonomously reviewing and validating the knowledge base of a never-ending learning system. In: Procedings of the 22nd International Conf on World Wide Web, pp. 1195–1204. ACM (2013)
Pedro, S.D., Hruschka Jr., E.R.: Collective intelligence as a source for machine learning self-supervision. In: Proceedings of the 4th International Workshop on Web Intelligence & Communities, p. 5. ACM (2012)
Pedro, S.D.S., Hruschka, E.R.: Conversing learning: active learning and active social interaction for human supervision in never-ending learning systems. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds.) IBERAMIA 2012. LNCS (LNAI), vol. 7637, pp. 231–240. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34654-5_24
Ronald, B., Levesque, H.: Knowledge representation and reasoning. Elsevier, New York (2004)
Wallace, R.: The Elements of Aiml Style. Alice AI Foundation (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
de Souza, W.W.O., Hruschka Júnior, E.R. (2017). Cognitive Conversation Language - CCL. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-53480-0_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53479-4
Online ISBN: 978-3-319-53480-0
eBook Packages: EngineeringEngineering (R0)