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Real versus Template-Based Natural Language Generation: A False Opposition?

Published:01 March 2005Publication History
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Abstract

This article challenges the received wisdom that template-based approaches to the generation of language are necessarily inferior to other approaches as regards their maintainability, linguistic well-foundedness, and quality of output. Some recent NLG systems that call themselves ''template-based'' will illustrate our claims.

References

  1. Becker, Tilman. 2002. Practical, template-based natural language generation with TAG. In Proceedings of TAG+6, Venice.Google ScholarGoogle Scholar
  2. Becker, Tilman and Stephan Busemann, editors. 1999. "May I Speak Freely?" Between Templates and Free Choice in Natural Language Generation: KI-99 Workshop. DFKI, Saarbrücken, Germany.Google ScholarGoogle Scholar
  3. Busemann, Stephan and Helmut Horacek. 1998. A flexible shallow approach to text generation. In Proceedings of the Ninth International Workshop on Natural Language Generation, pages 238-247: Niagara-on-the-Lake, Ontario, Canada.Google ScholarGoogle Scholar
  4. Cahill, Lynn, Christy Doran, Roger Evans, Chris Mellish, Daniel Paiva, Mike Reape, and Donia Scott. 1999. In search of a reference architecture for NLG systems. In Proceedings of the Seventh European Workshop on Natural Language Generation, pages 77-85: Toulouse, France.Google ScholarGoogle Scholar
  5. Dale, Robert and Ehud Reiter. 1995. Computational interpretations of the Gricean maxims in the generation of referring expressions.Cognitive Science, 18:233-263.Google ScholarGoogle Scholar
  6. Galley, Michel, Eric Fosler-Lussier, and Alexandros Potamianos. 2001. Hybrid natural language generation for spoken dialogue systems. In Proceedings of the Seventh European Conference on Speech Communication and Technology. Aalborg, Denmark.Google ScholarGoogle Scholar
  7. Geldof, Sabine and Walter van de Velde. 1997. An architecture for template based (hyper)text generation. In Proceedings of the Sixth European Workshop on Natural Language Generation, pages 28-37, Duisburg, Germany.Google ScholarGoogle Scholar
  8. Joshi, Aravind. 1987. The relevance of tree adjoining grammar to generation. In Gerard Kempen, editor. Natural Language Generation, Martinus Nijhoff, Leiden, The Netherlands, pages 233-252.Google ScholarGoogle Scholar
  9. Kittredge, Richard, Eli Goldberg, Myunghee Kim, and Alain Polgueère. 1994. Sublanguage engineering in the FOG system. In Fourth Conference on Applied Natural Language Processing, pages 215-216, Stuttgart, Germany. Google ScholarGoogle Scholar
  10. Krahmer, Emiel and Mariët Theune. 2002. Efficient context-sensitive generation of descriptions in context. In Kees van Deemter and Rodger Kibble, editors, Information Sharing. CSLI Publications, Stanford, CA, pages 223-264.Google ScholarGoogle Scholar
  11. Langkilde, Irene and Kevin Knight. 1998. Generation that exploits corpus-based statistical knowledge. In Proceedings of the ACL, pages 704-710, Montreal, Quebec, Canada. Google ScholarGoogle Scholar
  12. McRoy, Susan W., Songsak Channarukul, and Syed S. Ali. 2003. An augmented template-based approach to text realization. Natural Language Engineering, 9(4):381-420. Google ScholarGoogle Scholar
  13. Mellish, Chris. 2000. Understanding shortcuts in NLG systems. In Proceedings of Impacts in Natural Language Generation: NLG between Technology and Applications, pages 43-50, Dagstuhl, Germany.Google ScholarGoogle Scholar
  14. Piwek, Paul. 2003. A flexible pragmatics-driven language generator for animated agents. In Proceedings of EACL03 (Research Notes), pages 151-154, Budapest, Hungary. Google ScholarGoogle Scholar
  15. Reiter, Ehud. 1995. NLG vs. templates. In Proceedings of the Fifth European Workshop on Natural Language Generation, pages 95-105, Leiden, The Netherlands.Google ScholarGoogle Scholar
  16. Reiter, Ehud and Robert Dale. 1997. Building applied natural language generation systems. Natural Language Engineering, 3(1):57-87. Google ScholarGoogle Scholar
  17. Reiter, Ehud and Robert Dale. 2000. Building Natural Language Generation Systems. Cambridge University Press, Cambridge. Google ScholarGoogle Scholar
  18. Stenzhorn, Holger. 2002. A natural language generation system using XML- and Java-technologies. In Proceedings of the Second Workshop on NLP and XML, Taipei, Taiwan. Google ScholarGoogle Scholar
  19. Theune, Mariët, Esther Klabbers, Jan-Roelof de Pijper, Emiel Krahmer, and Jan Odijk. 2001. From data to speech: A general approach. Natural Language Engineering, 7(1):47-86. Google ScholarGoogle Scholar
  20. van Deemter, Kees and Jan Odijk. 1997. Context modelling and the generation of spoken discourse. Speech Communication, 21(1/2):101-121. Google ScholarGoogle Scholar
  21. Walker, Marilyn, Owen Rambow, and Monica Rogati. 2002. Training a sentence planner for spoken dialogue using boosting. Computer Speech and Language, 16:409-433.Google ScholarGoogle Scholar
  22. White, Michael and Ted Caldwell. 1998. EXEMPLARS: A practical, extensible framework for dynamic text generation. In Proceedings of the Ninth International Workshop on Natural Language Generation, pages 266-275, Niagara-on-the-Lake, Ontario, Canada.Google ScholarGoogle Scholar

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  1. Real versus Template-Based Natural Language Generation: A False Opposition?

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      • Published in

        cover image Computational Linguistics
        Computational Linguistics  Volume 31, Issue 1
        March 2005
        154 pages
        ISSN:0891-2017
        EISSN:1530-9312
        Issue’s Table of Contents

        Publisher

        MIT Press

        Cambridge, MA, United States

        Publication History

        • Published: 1 March 2005
        Published in coli Volume 31, Issue 1

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