Weighted alignment matrices for statistical machine translation

Current statistical machine translation sys-tems usually extract rules from bilingual corpora annotated with 1-best alignments. They are prone to learn noisy rules due to alignment mistakes. We propose a new structure called weighted alignment matrix to encode all possible alignments for a par-allel text compactly. The key idea is to as-sign a probability to each word pair to in-dicate how well they are aligned. We de-sign new algorithms for extracting phrase pairs from weighted alignment matrices and estimating their probabilities. Our ex-periments on multiple language pairs show that using weighted matrices achieves con-sistent improvements over using n-best lists in significant less extraction time.