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Phrase-based statistical machine translation: A level of detail approach

  • Hendra Setiawan*
  • , Haizhou Li
  • , Min Zhang
  • , Beng Chin Ooi
  • *Corresponding author for this work
  • Agency for Science, Technology and Research, Singapore
  • National University of Singapore

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The merit of phrase-based statistical machine translation is often reduced by the complexity to construct it. In this paper, we address some issues in phrase-based statistical machine translation, namely: the size of the phrase translation table, the use of underlying translation model probability and the length of the phrase unit. We present Level-Of-Detail (LOD) approach, an agglomerative approach for learning phrase-level alignment. Our experiments show that LOD approach significantly improves the performance of the word-based approach. LOD demonstrates a clear advantage that the phrase translation table grows only sub-linearly over the maximum phrase length, while having a performance comparable to those of other phrase-based approaches.

Original languageEnglish
Title of host publicationNatural Language Processing - IJCNLP 2005 - Second International Joint Conference, Proceedings
PublisherSpringer Verlag
Pages576-587
Number of pages12
ISBN (Print)3540291725, 9783540291725
DOIs
StatePublished - 2005
Externally publishedYes
Event2nd International Joint Conference on Natural Language Processing, IJCNLP 2005 - Jeju Island, Korea, Republic of
Duration: 11 Oct 200513 Oct 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3651 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Joint Conference on Natural Language Processing, IJCNLP 2005
Country/TerritoryKorea, Republic of
CityJeju Island
Period11/10/0513/10/05

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