A comparative study of hypothesis alignment and its improvement for machine translation system combination

Abstract

Recently confusion network decoding shows the best performance in combining outputs from multiple machine translation (MT) systems. However, overcoming different word orders presented in multiple MT systems during hypothesis alignment still remains the biggest challenge to confusion network-based MT system combination. In this paper, we compare four commonly used word alignment methods, namely GIZA++, TER, CLA and IHMM, for hypothesis alignment. Then we propose a method to build the confusion network from intersection word alignment, which utilizes both direct and inverse word alignment between the backbone and hypothesis to improve the reliability of hypothesis alignment. Experimental results demonstrate that the intersection word alignment yields consistent performance improvement for all four word alignment methods on both Chinese-to-English spoken and written language tasks.