Using Direct Variant Transduction for Rapid Development of Natural Spoken Interfaces

  • Hiyan Alshawi
  • Shona Douglas
Part of the Text, Speech and Language Technology book series (TLTB, volume 22)


Current speech-enabled services tend to fall into two categories: highly tuned systems requiring a large effort by specialized developers, or constrained systems that are developed rapidly but do not allow users to speak naturally. In this paper we present a new approach to language understanding aimed at bridging the gap between these extremes. This approach (direct variant transduction) relies on specifying an application with examples and on classification and pattern-matching techniques. It addresses two bottlenecks in the development of an interface with natural spoken language: coping with language variation and linking natural language to appropriate actions in the application back-end. Dialog control can be specified declaratively or be delegated to arbitrary functions computed by the underlying application. We describe the method and provide experimental results on varying the number of examples used to build a particular application.


Spoken language understanding language variation string matching text classification dialog control 


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  1. Alshawi, H. and Douglas, S. (2000). Learning dependency transduction models from unannotated examples. Philosophical Transactions of the Royal Society (Series A: Mathematical, Physical and Engineering Sciences), 358:1357–1372.CrossRefGoogle Scholar
  2. Aust, H., Oerder, M., Seide, F., and Steinbiss, V. (1995). The Philips automatic train timetable information system. Speech Communication, 17:249–262.CrossRefGoogle Scholar
  3. Chu-Carroll, J. and Carpenter, B. (1999). Vector-based natural language call routing. Computational Linguistic, 25(3):361–388.Google Scholar
  4. Dowding, J., Gawron, J. M., Appelt, D., Bear, J., Cherny, L., Moore, R., and Moran, D. (1994). Gemini: A natural language system for spoken-language understanding. In Proc. ARPA Human Language Technology Workshop’ 93, pages 43–48, Princeton, NJ.Google Scholar
  5. Gorin, A., Riccardi, G., and Wright, J. (1997). How may I help you? Speech Communication, 23(1–2): 113–127.CrossRefGoogle Scholar
  6. Levin, E. and Pieraccini, R. (1997). A stochastic model of computer-human interaction for learning dialogue strategies. In Proceedings of EUROSPEECH97, pages 1883–1886, Rhodes, Greece.Google Scholar
  7. Miller, S., Crystal, M., Fox, H., Ramshaw, L., Schwartz, R., Stone, R., Weischedel, R., and the Annotation Group (1998). Algorithms that learn to extract information — BBN: description of the SIFT system as used for MUC-7. In Proceedings of the Seventh Message Understanding Conference (MUC-7), Fairfax, VA. Morgan Kaufmann.Google Scholar
  8. Pereira, F., Tishby, N., and Lee, L. (1993). Distributional clustering of English words. In Proceedings of the 31st meeting of the Association for Computational Linguistics, pages 183–190.Google Scholar
  9. Quinlan, J. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.Google Scholar
  10. Schapire, R. E. and Singer, Y. (2000). BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2/3):135–168.CrossRefGoogle Scholar
  11. Sumita, E. and Iida, H. (1995). Heterogeneous computing for example-based translation of spoken language. In Proceedings of the 6th International Conference on Theoretical and Methodological Issues in Machine Translation, pages 273–286, Leuven, Belgium.Google Scholar
  12. Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer, New York.Google Scholar
  13. Wagner, R. A. and Fischer, M. J. (1974). The string-to-string correction problem. Journal of the Association for Computing Machinery, 21(1):168–173.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Hiyan Alshawi
    • 1
  • Shona Douglas
    • 1
  1. 1.AT&T Labs, Shannon LaboratoryFlorham ParkUSA

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