Improving Pivot-Based Statistical Machine Translation by Pivoting the Co-occurrence Count of Phrase Pairs

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

Abstract: To overcome the scarceness of bilingual corpora for some language pairs in machine translation, pivot-based SMT uses pivot language as a "bridge" to generate source-target translation from sourcepivot and pivot-target translation. One of the key issues is to estimate the probabilities for the generated phrase pairs. In this paper, we present a novel approach to calculate the translation probability by pivoting the co-occurrence count of source-pivot and pivot-target phrase pairs. Experimental results on Europarl data and web data show that our method leads to significant improvements over the baseline systems.