Introduction

doi:10.1017/CBO9780511844492.001

1 - Introduction

Published online by Cambridge University Press: 05 July 2014

Amanda Stent and

Srinivas Bangalore

Edited by

Amanda Stent and

Srinivas Bangalore

Show author details

Amanda Stent: Affiliation:
AT&T Research, Florham Park, New Jersey
Srinivas Bangalore: Affiliation:
AT&T Research, Florham Park, New Jersey

Book contents

Get access

Summary

The story of Pinocchio is the story of a man who creates a puppet with the ability to speak using human language, and to converse naturally – even to lie. Since the early days of the industrial revolution, people have imagined creating agents that can emulate human skills, including language production and conversation (Mayer, 1878; Wood, 2003). A modern example of this is Turing's test for artificial intelligence – a machine that is indistinguishable from a human being when evaluated through disembodied conversation (Turing, 1950).

This book concerns the intersection of two areas of computational research involved in the production of conversational agents – natural language generation and interactive systems.

Natural language generation

Natural language generation (NLG) systems are systems that produce human language artifacts (including speech, text, and language-rich multimedia presentations). NLG systems differ in the inputs they take, the types of output they produce and the degree to which they support interactivity. However, there are some common challenges across all types of NLG system. All end-to-end NLG systems perform the following three tasks: content selection, or determination of “what to say”; surface realization, or determination of “how to say it” (including assignment of content to media, selection of words, and arrangement of the content); and production, or the presentation/performance of the generated material (which in the case of an embodied agent may include production of body postures, gestures, and sign language).

Type: Chapter
Information: Natural Language Generation in Interactive Systems , pp. 1 - 10

DOI: https://doi.org/10.1017/CBO9780511844492.001 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

André, E., Rist, T., and Baldes, S. (2001). From simulated dialogues to interactive performances. In Multi-Agent-Systems and Applications II, pages 107-118. Springer, Berlin, Germany.Google Scholar

Cassell, J. (2000). Embodied conversational interface agents. Communications of the ACM, 43(4):70-78.CrossRef Google Scholar

Clark, H. (1996). Using Language. Cambridge University Press, Cambridge, UK.CrossRef Google Scholar

Cox, R., O'Donnell, M., and Oberlander, J. (1999). Dynamic versus static hypermedia in museum education: An evaluation of ILEX, the intelligent labelling explorer. In Proceedings of the Conference on Artificial Intelligence in Education, pages 181-188, Le Mans, France. International Artificial Intelligence in Education Society.Google Scholar

Di Fabbrizio, G., Gupta, N., Besana, S., and Mani, P. (2010). Have2eat: A restaurant finder with review summarization for mobile phones. In Proceedings of the International Conference on Computational Linguistics (COLING), pages 17-20, Beijing, China. International Committee on Computational Linguistics.Google Scholar

Grice, H. P. (1975). Logic and conversations. In Cole, P. and Morgan, J. L., editors, Syntax and Semantics III: Speech Acts, pages 41-58. Academic Press, New York, NY.Google Scholar

Kilger, A. and Finkler, W. (1995). Incremental generation for real-time applications. Technical Report DFKI Report RR-95-11, German Research Center for Artificial Intelligence — DFKI GmbH, Saarbrücken, Germany.Google Scholar

Koller, A., Striegnitz, K., Byron, D., Cassell, J., Dale, R., and Moore, J. D. (2010). The first challenge on generating instructions in virtual environments. In Krahmer, E. and Theune, M., editors, Empirical Methods in Natural Language Generation, pages 328-352. Springer LNCS, Berlin, Germany.Google Scholar

Levelt, W. J. M. (1989). Speaking: From Intention to Articulation. MIT Press, Cambridge, MA.Google Scholar

Mayer, A. M. (1878). On Edison's talking-machine. Popular Science Monthly, 12:719-723.Google Scholar

Molina, M., Parodi, E., and Stent, A. (2011). Using the journalistic metaphor to design user interfaces that explain sensor data. In Proceedings of INTERACT, pages 636-643, Lisbon, Portugal. Springer.Google Scholar

Rambow, O., Bangalore, S., and Walker, M. (2001). Natural language generation in dialog systems. In Proceedings of the Human Language Technology Conference (HLT), pages 270-273, San Diego, CA. Association for Computational Linguistics.Google Scholar

Reiter, E. and Dale, R. (2000). Building Natural Language Generation Systems. Cambridge University Press, Cambridge, UK.CrossRef Google Scholar

Reiter, E., Gatt, A., Portet, F., and van der Meulen, M. (2008). The importance of narrative and other lessons from an evaluation of an NLG system that summarises clinical data. In Proceedings of the International Conference on Natural Language Generation (INLG), pages 147-156, Salt Fork, OH. Association for Computational Linguistics.Google Scholar

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59:433-460.Google Scholar

Walker, M., Whittaker, S., Stent, A., Maloor, P., Moore, J. D., Johnston, M., and Vasireddy, G. (2002). Speech-Plans: Generating evaluative responses in spoken dialogue. In Proceedings of the International Conference on Natural Language Generation (INLG). Association for Computational Linguistics.Google Scholar

Wood, G. (2003). Edison's Eve: A magical history of the quest for mechanical life. Anchor Books, New York, NY.Google Scholar