Abstract

Most statistical translation systems are based on phrase translation pairs, or “blocks”, which are obtained mainly from word alignment. We use blocks to infer better word alignment and improved word alignment which, in turn, leads to better inference of blocks. We propose two new probabilistic models based on the innerouter segmentations and use EM algorithms for estimating the models ’ parameters. The first model recovers IBM Model-1 as a special case. Both models outperform bidirectional IBM Model-4 in terms of word alignment accuracy by 10 % absolute on the F-measure. Using blocks obtained from the models in actual translation systems yields statistically significant improvements in Chinese-English SMT evaluation. 1

Citations