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We present results and experiences from our experiments with phrase-based statistical machine translation using Moses. The paper is based on the idea of using an off-the-shelf parser to supply linguistic information to a factored translation model and compare the results of German---English translation to the shared task baseline system based on word form. We report partial results for this model and results for two simplified setups. Our best setup takes advantage of the parser's lemmatization and decompounding. A qualitative analysis of compound translation shows that decompounding improves translation quality.
Holmqvist et al. (Mon,) studied this question.
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