Advertisement

Interfacing an English Text Generator with a German MT Analysis

  • John Bateman
  • Robert Kasper
  • Jörg Schütz
  • Erich Steiner
Part of the Informatik-Fachberichte book series (INFORMATIK, volume 238)

Abstract

In this paper we investigate a strategy for using an existing English text generator to serve as the generation module in a machine translation (MT) system for translating German into English. The English text generator has been designed to take requests for text construction in a specified interface language and, making reference to domain knowledge as necessary, to produce appropriate multi-sentential English text; this generator is therefore said to be knowledge based. In contrast, the MT analysis result that is used as input to the translation process is based upon semanticaily-interpreted canonical dependency structure (interface structure), there is no direct link to domain knowledge of any kind. The conceptual link that makes the co-operation possible is a particular interpretation of linguistic information as a kind of (linguistic) knowledge base. The characteristics necessary for an interface between two such systems represent a particular challenge, but they also constitute the main interest in the project, implying that projects can be successfully interfaced, providing there are appropriate conceptual links between the systems involved. These links mainly have to do with the embodied notions of linguistic structure. Such links also appear to lead to useful observations concerning the theory of (machine) translation.

Keywords:

machine translation generation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Arnold, D.J. and L. des Tombe. Basic theory and methodology in Eurotra. In Nirenburg, S. ed. pp. 114–135, 1987.Google Scholar
  2. Arnold, D.J., S. Krauwer, M. Rosner, L. des Tombe and G.B. Varile. The <C,A>,T framework in Eurotra: A theoretically committed notation for MT. In Proceedings of COLING-86, pp. 297–303, 1986Google Scholar
  3. [3]
    Carbonell, J.G. and M. Tomita. Knowledge-based machine translation: The CMU approach. In Nirenburg, S. ed. 1987.Google Scholar
  4. Fawcett, R.P. The semantics of clause and verb for relational processes in English. In Hailiday, M.A.K. and R.P. Fawcett eds. New Developments in Systemic Linguistics, Vol. 1, London: Frances Pinter, 1987Google Scholar
  5. [5]
    Halliday, M.A.K. An Introduction to Functional Grammar. London: Edward Arnold, 1985.Google Scholar
  6. [6]
    Houghton, G. and S. Isard. Why to speak, what to say, and how to say it. In Morris, P. ed. Models of Cognition. New York: Wiley, 1987.Google Scholar
  7. [7]
    Kasper, R. Feature Structures: A Logical Theory with Application to Language Analysis. PhD dissertation, University of Michigan, 1987.Google Scholar
  8. Kasper, R. and R. Whitney. SPL: A Sentence Plan Language for Text Generation. USC/Information Sciences Institute Research Report, (forthcoming).Google Scholar
  9. Mann, W.C. An Overview of the Penman Text Generation System. In Proceedings of AAAI-83, pp. 261–265, August 1983. Also available as ISI Research Report, ISI/RR-83–114.Google Scholar
  10. [10]
    Mann, W.C. and C. Matthiessen. Nigel: A Systemic Grammar for Text Generation. USC/Information Sciences Institute, RR-83–105. Also appears in R. Benson and J. Greaves, editors, Systemic Perspectives on Discourse: Selected Papers Papers from the Ninth International Systemics Workshop, Ablex, London, England, 1985.Google Scholar
  11. [11]
    Matthiessen, C. Choosing tense in English. ISI Research Report, ISI/RR-84–143, 1984.Google Scholar
  12. [12]
    Mellish, C. Implementing Systemic Classification by Unification. In Computational Linguistics, Vol. 14:1, pp. 40–51, 1988.Google Scholar
  13. [13]
    Nirenburg, S. ed. Machine Translation: Theoretical and Methodological Issues. Cambridge: Cambridge University Press, 1987.Google Scholar
  14. [14]
    Patten, T. Systemic Text Generation as Problem Solving. Cambridge: Cambridge University Press, 1988.CrossRefzbMATHGoogle Scholar
  15. [15]
    Patten, T. and G. Ritchie. Towards a formal model for systemic grammar. In Kempen, G. ed. Natural Language Generation. Dordrecht: Martinus Nijhoff, 1987.Google Scholar
  16. [16]
    Rösner, D. When Mariko talks to Siegfried: Experiences from a Japanese/German MT Project. In Proceedings of COLING-86, pp. 652–654, 1986.Google Scholar
  17. [17]
    Schütz, J. CAT2 - Ein Formalismus für multilinguale maschinelle Übersetzung und seine Implementierung. In Proceedings of Computer und Sprache, Universität des Saarlandes, 1988.Google Scholar
  18. [18]
    Schütz, J. and R. Sharp. CAT2-R, Komplexität eines Formalismus für multilinguale maschinelle Ubersetzung. Saarbrücken: IAI Working Papers No. 6, 1988.Google Scholar
  19. [19]
    Sharp, R. CAT2 - Implementing a formalism for multi-lingual machine translation. In Proceedings of the second Conference on theoretical and methodological issues in machine translation of natural languages, Pittsburgh, 1988.Google Scholar
  20. [20]
    Somers, H.L. The need for MT-oriented versions of case and valence in MT. In Proceedings of COLING- 86, pp. 118–123, 1986.Google Scholar
  21. [21]
    Steiner, E., P. Schmidt and C. Zelinsky-Wibbelt eds. From Syntax to Semantics: Insights from Machine Translation. London: Frances Pinter & Norwood, N.J.: Ablex, 1988.Google Scholar
  22. [22]
    Steiner, E. and J. Schütz. An. outline of the ET-D/Nigel Co-operation. Saarbrücken: IAI Working Papers No. 6, 1988.Google Scholar
  23. Van Eynde, F. The analysis of tense and aspect in Eurotra. In Proceedings of COLING-88, Vol. 2, pp. 699–704, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • John Bateman
    • 1
  • Robert Kasper
    • 1
  • Jörg Schütz
    • 2
  • Erich Steiner
    • 2
  1. 1.Information Sciences InstituteUniversity of Southern CaliforniaMarina del ReyUSA
  2. 2.Institut für Angewandte InformationsforschungUniversität des SaarlandesSaarbrückenGermany

Personalised recommendations