Transformation from Discontinuous to Continuous Word Alignment Improves Translation Quality

We present a novel approach to improve word alignment for statistical machine translation (SMT). Conventional word alignment methods allow discontinuous alignment, meaning that a source (or target) word links to several target (or source) words whose positions are discontinuous. However, we cannot extract phrase pairs from this kind of alignments as they break the alignment consistency constraint. In this paper, we use a weighted vote method to transform discontinuous word alignment to continuous alignment, which enables SMT systems extract more phrase pairs. We carry out experiments on large scale Chineseto-English and German-to-English translation tasks. Experimental results show statistically significant improvements of BLEU score in both cases over the baseline systems. Our method produces a gain of +1.68 BLEU on NIST OpenMT04 for the phrase-based system, and a gain of +1.28 BLEU on NIST OpenMT06 for the hierarchical phrase-based system.