Getting the Structure Right for Word Alignment: LEAF
Alexander Fraser, Daniel Marcu · 2007
Word alignment is the problem of annotating parallel text with translational correspon-dence. Previous generative word alignment models have made structural assumptions such as the 1-to-1, 1-to-N, or phrase-based consecutive word assumptions, while previ-ous discriminative models have either made such an assumption directly or used features derived from a generative model making one of these assumptions. We present a new gen-erative alignment model which avoids these structural limitations, and show that it is effective when trained using both unsuper-vised and semi-supervised training methods. 1