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This paper addresses the problem of morphological modeling in statistical speech-to-speech translation for English to Iraqi Arabic. An analysis of user data from a real-time MT-based dialog system showed that generating correct verbal inflections is a key problem for this language pair. We approach this problem by enriching the training data with morphological information derived from source-side dependency parses. We analyze the performance of several parsers as well as the effect on different types of translation models. Our method achieves an improvement of more than a full BLEU point and a significant increase in verbal inflection accuracy; at the same time, it is computationally inexpensive and does not rely on target-language linguistic tools.

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