Generating Complex Morphology for Machine Translation

We present a novel method for predicting inflected word forms for generating morphologically rich languages in machine translation. We utilize a rich set of syntactic and morphological knowledge sources from both source and target sentences in a probabilistic model, and evaluate their contribution in generating Russian and Arabic sentences. Our results show that the proposed model substantially outperforms the commonly used baseline of a trigram target language model; in particular, the use of morphological 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.

• Type: Conference
• Year: 2007
• Where: ACL
• Authors: Einat Minkov, Kristina Toutanova, Hisami Suzuki
• Keywords: ACL 2007, Computational Linguistics, Morphological Knowledge Sources, Target Language Model, Target Sentences