- Aaron B. Phillips, V. Cavalli-Sforza, Ralf D. Brown. 2007. Improving example-based machine translation through morphological generalization and adaptation. Abstract: Example Based Machine Translation (EBMT) is limited by the quantity and scope of its training data. Ev en with a reasonably large corpus, we will not have examples that cover everyt hing we want to translate. This problem is especial ly severe in Arabic due to its rich morphology. We demonstrate a novel method that exploits the regular nature of Arabic morphology t o increase the quality and coverage of machine translation. Through the use of generalization and rewrite rules, we are able to r ecover the English translation of phrases that do not exist in the training corpora. Furthermore, this system shows improvement in BLEU even with a training corpus of 1.4 million sentence pairs.
