Semi Supervised Training for Statistical Word Alignment
We introduce a semi supervised approach to training for statistical machine translation that alternates the traditional Expectation Maximization step that is applied on a large training corpus with a discriminative step aimed at increasing word alignment quality on a small, manually word aligned sub corpus. We show that our algorithm leads not only to improved alignments but also to machine translation outputs of higher quality.
ACL