The use of a structural n-gram language model in generation-heavy hybrid machine translation

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper describes the use of a statistical structural N-gram model in the natural language generation component of a Spanish-English generation-heavy hybrid machine translation system. A structural N-gram model captures the relationship between words in a dependency representation without taking into account the overall structure at the phrase level. The model is used together with other components in the system for lexical and structural selection. An evaluation of the machine translation system shows that the use of structural N-grams decreases runtime by 60% with no loss in translation quality.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAnja Belz, Roger Evans, Paul Piwek
PublisherSpringer Verlag
Pages61-69
Number of pages9
ISBN (Print)3540223401, 9783540223405
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3123
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'The use of a structural n-gram language model in generation-heavy hybrid machine translation'. Together they form a unique fingerprint.

  • Cite this

    Habash, N. (2004). The use of a structural n-gram language model in generation-heavy hybrid machine translation. In A. Belz, R. Evans, & P. Piwek (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 61-69). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3123). Springer Verlag. https://doi.org/10.1007/978-3-540-27823-8_7