Revisiting pivot language approach for machine translation

This paper revisits the pivot language ap-proach for machine translation. First, we investigate three different methods for pivot translation. Then we employ a hybrid method combining RBMT and SMT systems to fill up the data gap for pivot translation, where the source-pivot and pivot-target corpora are inde-pendent. Experimental results on spo-ken language translation show that this hybrid method significantly improves the translation quality, which outperforms the method using a source-target corpus of the same size. In addition, we pro-pose a system combination approach to select better translations from those pro-duced by various pivot translation meth-ods. This method regards system com-bination as a translation evaluation prob-lem and formalizes it with a regression learning model. Experimental results in-dicate that our method achieves consistent and significant improvement over individ-ual translation outputs.