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Towards developing generation algorithms for text-to-text applications

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Daniel MarcuAuthors Info & Claims

ACL '05: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics

Pages 66 - 74

https://doi.org/10.3115/1219840.1219849

Published: 25 June 2005 Publication History

Abstract

We describe a new sentence realization framework for text-to-text applications. This framework uses IDL-expressions as a representation formalism, and a generation mechanism based on algorithms for intersecting IDL-expressions with probabilistic language models. We present both theoretical and empirical results concerning the correctness and efficiency of these algorithms.

References

[1]

Srinivas Bangalore and Owen Rambow. 2000. Using TAG, a tree model, and a language model for generation. In Proceedings of the 1st International Natural Language Generation Conference.

[2]

Regina Barzilay. 2003. Information Fusion for Multi-document Summarization: Paraphrasing and Generation. Ph.D. thesis, Columbia University.

Digital Library

[3]

Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993. The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2):263--311.

Digital Library

[4]

Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. 2001. Introduction to Algorithms. The MIT Press and McGraw-Hill. Second Edition.

Digital Library

[5]

Simon Corston-Oliver, Michael Gamon, Eric K. Ringger, and Robert Moore. 2002. An overview of Amalgam: A machine-learned generation module. In Proceedings of the International Natural Language Generation Conference.

[6]

Michael Elhadad. 1991. FUF User manual --- version 5.0. Technical Report CUCS-038-91, Department of Computer Science, Columbia University.

[7]

John E. Hopcroft and Jeffrey D. Ullman. 1979. Introduction to automata theory, languages, and computation. Addison-Wesley.

Digital Library

[8]

Kevin Knight and Jonathan Graehl. 1998. Machine transliteration. Computational Linguistics, 24(4):599--612.

Digital Library

[9]

Kevin Knight and Vasileios Hatzivassiloglou. 1995. Two level, many-path generation. In Proceedings of the Association of Computational Linguistics.

Digital Library

[10]

Kevin Knight. 1999. Decoding complexity in word-replacement translation models. Computational Linguistics, 25(4):607--615.

Digital Library

[11]

Irene Langkilde-Geary. 2002. A foundation for general-purpose natural language generation: sentence realization using probabilistic models of language. Ph.D. thesis, University of Southern California.

Digital Library

[12]

Christian Matthiessen and John Bateman. 1991. Text Generation and Systemic-Functional Linguistic. Pinter Publishers, London.

[13]

Mehryar Mohri, Fernando Pereira, and Michael Riley. 2002. Weighted finite-state transducers in speech recognition. Computer Speech and Language, 16(1):69--88.

Digital Library

[14]

Mark-Jan Nederhof and Giorgio Satta. 2004. IDL-expressions: a formalism for representing and parsing finite languages in natural language processing. Journal of Artificial Intelligence Research, 21:287--317.

[15]

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a method for automatic evaluation of machine translation. In Proceedings of the Association for Computational Linguistics (ACL-2002), pages 311--318, Philadelphia, PA, July 7--12.

Digital Library

[16]

Stuart Russell and Peter Norvig. 1995. Artifi cial Intelligence. A Modern Approach. Prentice Hall, Englewood Cliffs, New Jersey.

Digital Library

Cited By

Wan SDras MDale RParis C(2010)Spanning tree approaches for statistical sentence generationEmpirical methods in natural language generation10.5555/1880370.1880373(13-44)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.5555/1880370.1880373
Wann SDras MDale RParis CLascarides AGardent CNivre J(2009)Improving grammaticality in statistical sentence generationProceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics10.5555/1609067.1609162(852-860)Online publication date: 30-Mar-2009
https://dl.acm.org/doi/10.5555/1609067.1609162
Wan SDale RDras MParis CLapata MNg H(2008)Seed and GrowProceedings of the Conference on Empirical Methods in Natural Language Processing10.5555/1613715.1613782(543-552)Online publication date: 25-Oct-2008
https://dl.acm.org/doi/10.5555/1613715.1613782
Show More Cited By

Towards developing generation algorithms for text-to-text applications
1. Hardware
  1. Power and energy
    1. Power estimation and optimization

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Published In

cover image DL Hosted proceedings

ACL '05: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics

June 2005

657 pages

General Chair:
Kevin Knight
University of Southern California

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 25 June 2005

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ACL '05 Paper Acceptance Rate 77 of 423 submissions, 18%;

Overall Acceptance Rate 85 of 443 submissions, 19%

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Cited By

Wan SDras MDale RParis C(2010)Spanning tree approaches for statistical sentence generationEmpirical methods in natural language generation10.5555/1880370.1880373(13-44)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.5555/1880370.1880373
Wann SDras MDale RParis CLascarides AGardent CNivre J(2009)Improving grammaticality in statistical sentence generationProceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics10.5555/1609067.1609162(852-860)Online publication date: 30-Mar-2009
https://dl.acm.org/doi/10.5555/1609067.1609162
Wan SDale RDras MParis CLapata MNg H(2008)Seed and GrowProceedings of the Conference on Empirical Methods in Natural Language Processing10.5555/1613715.1613782(543-552)Online publication date: 25-Oct-2008
https://dl.acm.org/doi/10.5555/1613715.1613782
Soricut RMarcu D(2006)Discourse generation using utility-trained coherence modelsProceedings of the COLING/ACL on Main conference poster sessions10.5555/1273073.1273176(803-810)Online publication date: 17-Jul-2006
https://dl.acm.org/doi/10.5555/1273073.1273176
Soricut RMarcu D(2006)Stochastic language generation using WIDL-expressions and its application in machine translation and summarizationProceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics10.3115/1220175.1220314(1105-1112)Online publication date: 17-Jul-2006
https://dl.acm.org/doi/10.3115/1220175.1220314

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