Getting the Structure Right for Word Alignment: LEAF

Alexander M. Fraser, Daniel Marcu

Abstract: Word alignment is the problem of annotating parallel text with translational correspondence. 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 previous 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 generative alignment model which avoids these structural limitations, and show that it is effective when trained using both unsupervised and semi-supervised training methods.