Morphological Modeling for Machine Translation of English-Iraqi Arabic Spoken Dialogs.

This paper addresses the problem of morphological modeling in statistical speech-tospeech 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 sourceside 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.