Please use this identifier to cite or link to this item:
|Title:||Phrase-based statistical machine translation: A level of detail approach|
|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)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 14, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.