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Towards incorporating language morphology into statistical machine translation systems
IEEE Workshop on Automatic Speech Recognition and Understanding, 2005.
In this paper, a novel algorithm for incorporating morphological knowledge into statistical machine translation (SMT) systems is proposed. First, word stems are acquired automatically for the source and target languages using an unsupervised morphological acquisition algorithm. Then a word-stem based SMT system is built and combined with a phrase-based word level SMT system using a general statistical framework. The combined lexical and morphological SMT system is implemented using latedoi:10.1109/asru.2005.1566533 fatcat:azx6gi66vbb7lo3kzus3duenme