Paper: Phrase-Based Statistical Machine Translation: A Level of Detail Approach

ACL ID I05-1051
Title Phrase-Based Statistical Machine Translation: A Level of Detail Approach
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2005
Authors

The merit of phrase-based statistical machine translation is often reduced by the complexity to construct it. In this paper, we ad- dress some issues in phrase-based statistical machine translation, namely: the size of the phrase translation table, the use of underlying transla- tion model probability and the length of the phrase unit. We present Level-Of-Detail (LOD) approach, an agglomerative approach for learn- ing 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 per- formance comparable to those of other phrase-based approaches.