article

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

Authors:

Kees Van Deemter,

Mariët TheuneAuthors Info & Claims

Computational Linguistics, Volume 31, Issue 1

Pages 15 - 24

https://doi.org/10.1162/0891201053630291

Published: 01 March 2005 Publication History

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.

[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.

[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.

[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.

[5]

Dale, Robert and Ehud Reiter. 1995. Computational interpretations of the Gricean maxims in the generation of referring expressions.Cognitive Science, 18:233-263.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[14]

Piwek, Paul. 2003. A flexible pragmatics-driven language generator for animated agents. In Proceedings of EACL03 (Research Notes), pages 151-154, Budapest, Hungary.

[15]

Reiter, Ehud. 1995. NLG vs. templates. In Proceedings of the Fifth European Workshop on Natural Language Generation, pages 95-105, Leiden, The Netherlands.

[16]

Reiter, Ehud and Robert Dale. 1997. Building applied natural language generation systems. Natural Language Engineering, 3(1):57-87.

[17]

Reiter, Ehud and Robert Dale. 2000. Building Natural Language Generation Systems. Cambridge University Press, Cambridge.

[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.

[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.

[20]

van Deemter, Kees and Jan Odijk. 1997. Context modelling and the generation of spoken discourse. Speech Communication, 21(1/2):101-121.

[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.

[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.

Cited By

Musto CSpillo GSemeraro G(2024)Harnessing distributional semantics to build context-aware justifications for recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09382-x34:3(659-690)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11257-023-09382-x
Jing LSong XLin XZhao ZZhou WNie L(2023)Stylized Data-to-text Generation: A Case Study in the E-Commerce DomainACM Transactions on Information Systems10.1145/360337442:1(1-24)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1145/3603374
Mahlaza ZKeet C(2023)Surface Realization Architecture for Low-resourced African LanguagesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/356759422:3(1-26)Online publication date: 10-Mar-2023
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Index Terms

Real versus Template-Based Natural Language Generation: A False Opposition?
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
      2. Natural language generation

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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: 01 March 2005

Published in COLI Volume 31, Issue 1

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Cited By

Musto CSpillo GSemeraro G(2024)Harnessing distributional semantics to build context-aware justifications for recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09382-x34:3(659-690)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11257-023-09382-x
Jing LSong XLin XZhao ZZhou WNie L(2023)Stylized Data-to-text Generation: A Case Study in the E-Commerce DomainACM Transactions on Information Systems10.1145/360337442:1(1-24)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1145/3603374
Mahlaza ZKeet C(2023)Surface Realization Architecture for Low-resourced African LanguagesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/356759422:3(1-26)Online publication date: 10-Mar-2023
https://dl.acm.org/doi/10.1145/3567594
Li CChrysostomou DYang H(2023)A speech-enabled virtual assistant for efficient human–robot interaction in industrial environmentsJournal of Systems and Software10.1016/j.jss.2023.111818205:COnline publication date: 17-Oct-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111818
Demir S(2022)Turkish Data-to-Text Generation Using Sequence-to-Sequence Neural NetworksACM Transactions on Asian and Low-Resource Language Information Processing10.1145/354382622:2(1-27)Online publication date: 27-Dec-2022
https://dl.acm.org/doi/10.1145/3543826
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https://dl.acm.org/doi/10.1007/s11042-021-11186-3
Pavlopoulos JKougia VAndroutsopoulos IPapamichail D(2022)Diagnostic captioning: a surveyKnowledge and Information Systems10.1007/s10115-022-01684-764:7(1691-1722)Online publication date: 1-Jul-2022
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Zhang YRen Pde Rijke M(2021)A taxonomy, data set, and benchmark for detecting and classifying malevolent dialogue responsesJournal of the Association for Information Science and Technology10.1002/asi.2449672:12(1477-1497)Online publication date: 8-Nov-2021
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Madugalla AMarriott KMarinai SCapobianco SGoncu C(2020)Creating Accessible Online Floor Plans for Visually Impaired ReadersACM Transactions on Accessible Computing10.1145/341044613:4(1-37)Online publication date: 15-Oct-2020
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Kim BChoi YHung CCerny TShin DBechini A(2020)Automatic baseball commentary generation using deep learningProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3374063(1056-1065)Online publication date: 30-Mar-2020
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