Language Independent Connectivity Strength Features for Phrase Pivot Statistical Machine Translation

ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, Sofia, Bulgaria: 4 – 9 August, 2013
Language Independent Connectivity Strength Features for Phrase Pivot Statistical Machine Translation
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Abstract

An important challenge to statistical machine translation (SMT) is the lack of parallel data for many language pairs. One common solution is to pivot through a third language for which there exist parallel corpora with the source and target languages. Although pivoting is a robust technique, it introduces some low quality translations. In this paper, we present two language-independent features to improve the quality of phrase-pivot based SMT. The features, source connectivity strength and target connectivity strength reflect the quality of projected alignments between the source and target phrases in the pivot phrase table. We show positive results (0.6 BLEU points) on Persian-Arabic SMT as a case study.

 

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