Paper: Language Independent Connectivity Strength Features for Phrase Pivot Statistical Machine Translation

ACL ID P13-2073
Title Language Independent Connectivity Strength Features for Phrase Pivot Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

An important challenge to statistical ma- chine translation (SMT) is the lack of par- allel data for many language pairs. One common solution is to pivot through a third language for which there exist par- allel 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.