In modern machine translation practice, a sta- tistical phrasal or hierarchical translation sys- tem usually relies on a huge set of trans- lation rules extracted from bi-lingual train- ing data. This approach not only results in space and efficiency issues, but also suffers from the sparse data problem. In this paper, we propose to use factorized grammars, an idea widely accepted in the field of linguis- tic grammar construction, to generalize trans- lation rules, so as to solve these two prob- lems. Wedesignedamethodtotakeadvantage of the XTAG English Grammar to facilitate the extraction of factorized rules. We experi- mented on various setups of low-resource lan- guage translation, and showed consistent sig- nificant improvement in BLEU over state-of- the-art string-to-dependency baseli...

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