Improving Pivot-Based Statistical Machine Translation Using Random Walk

Xiaoning Zhu, Zhongjun He, Hua Wu, Haifeng Wang, Conghui Zhu, Tiejun Zhao

Abstract: This paper proposes a novel approach that utilizes 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 generate source-target translation. However, one of the weaknesses is that some useful sourcetarget translations cannot be generated if the corresponding source phrase and target phrase connect to different pivot phrases. To alleviate the problem, we utilize Markov random walks to connect possible translation phrases between source and target language. Experimental 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.