Improving Pivot-Based Statistical Machine Translation by Pivoting the Co-occurrence Count of Phrase Pairs
Xiaoning Zhu, Zhongjun He, Hua Ren Wu, Conghui Zhu, Haifeng Wang, Tiejun Zhao · 2014
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.