Please use this identifier to cite or link to this item: https://doi.org/10.1007/11562214_51
Title: Phrase-based statistical machine translation: A level of detail approach
Authors: Setiawan, H.
Li, H.
Zhang, M.
Ooi, B.C. 
Issue Date: 2005
Source: Setiawan, H.,Li, H.,Zhang, M.,Ooi, B.C. (2005). Phrase-based statistical machine translation: A level of detail approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3651 LNAI : 576-587. ScholarBank@NUS Repository. https://doi.org/10.1007/11562214_51
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. © Springer-Verlag Berlin Heidelberg 2005.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41769
ISBN: 3540291725
ISSN: 03029743
DOI: 10.1007/11562214_51
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