Revisiting Pivot Language Approach for Machine Translation

This paper revisits the pivot language approach 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 independent. Experimental results on spoken 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 propose a system combination approach to select better translations from those produced by various pivot translation methods. This method regards system combination as a translation evaluation problem and formalizes it with a regression learning model. Experimental results indicate that our method achieves consistent and significant improvement over individual translation outputs.