This talk will review the state of the art in statistical machine translation (SMT), and highlight some of my past and ongoing work towards syntax-based SMT.
The currently best methods in SMT build on the translation of phrases (any sequences of words) instead of single words. Phrase translation pairs are automatically learned from parallel corpora. While SMT systems generate translation output that often conveys a lot of the meaning of the original text, it is frequently ungrammatical and incoherent.
The research challenge at this point is to introduce syntactic knowledge to the state of the art in order to improve translation quality. I propose to break up the translation process along linguistic lines: My thesis work handled the translation of complex noun phrases. In ongoing work, I am addressing the challenges of translation at the clause level.