Paper: Bridging The Inflection Morphology Gap For Arabic Statistical Machine Translation

ACL ID N06-2051
Title Bridging The Inflection Morphology Gap For Arabic Statistical Machine Translation
Venue Human Language Technologies
Session Short Paper
Year 2006
Authors

Statistical machine translation (SMT) is based on the ability to effectively learn word and phrase relationships from par- allel corpora, a process which is consid- erably more difficult when the extent of morphological expression differs signifi- cantly across the source and target lan- guages. We present techniques that se- lect appropriate word segmentations in the morphologically rich source language based on contextual relationships in the target language. Our results take ad- vantage of existing word level morpho- logical analysis components to improve translation quality above state-of-the-art on a limited-data Arabic to English speech translation task.