Improving Pivot-Based Statistical Machine Translation Using Random Walk

* This work was done when the first author was visiting Baidu. This paper proposes a novel approach that uti-lizes a machine learning method to improve pivot-based statistical machine translation (SMT). For language pairs with few bilingual data, a possible solution in pivot-based SMT using another language as a "bridge " to gen-erate source-target translation. However, one of the weaknesses is that some useful source-target translations cannot be generated if the corresponding source phrase and target phrase connect to different pivot phrases. To allevi-ate the problem, we utilize Markov random walks to connect possible translation phrases between source and target language. Experi-mental results on European Parliament data, spoken language data and web data show that our method leads to significant improvements on all the tasks over the baseline system.