Paper: Generating Complex Morphology for Machine Translation

ACL ID P07-1017
Title Generating Complex Morphology for Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007

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.