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.
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Setiawan, H., Li, H., Zhang, M., Ooi, B.C. (2005). Phrase-Based Statistical Machine Translation: A Level of Detail Approach. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_51
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DOI: https://doi.org/10.1007/11562214_51
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