Generating Complex Morphology for Machine Translation

We present a novel method for predicting in-flected word forms for generating morpho-logically rich languages in machine trans-lation. We utilize a rich set of syntactic and morphological knowledge sources from both source and target sentences in a prob-abilistic model, and evaluate their contribu-tion in generating Russian and Arabic sen-tences. Our results show that the proposed model substantially outperforms the com-monly used baseline of a trigram target lan-guage model; in particular, the use of mor-phological and syntactic features leads to large gains in prediction accuracy. We also show that the proposed method is effective with a relatively small amount of data.