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.
To resolve this disadvantage, REF proposed the approach to calculate the cooccurrence count of source-pivot and pivot-target phrase pairs.
Improving Pivot-Based Statistical Machine Translation by Pivoting the Co-occurrence Count of Phrase Pairs
EMNLP
Computer Science