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

ACL ID D14-1174
Title Improving Pivot-Based Statistical Machine Translation by Pivoting the Co-occurrence Count of Phrase Pairs
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

To overcome the scarceness of bilingual corpora for some language pairs in ma- chine translation, pivot-based SMT uses pivot language as a "bridge" to generate source-target translation from source- pivot and pivot-target translation. One of the key issues is to estimate the probabili- ties 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.