Ensemble Triangulation for Statistical Machine Translation
Majid Razmara, Anoop Sarkar · 2013
State-of-the-art statistical machine transla-tion systems rely heavily on training data and insufficient training data usually re-sults in poor translation quality. One so-lution to alleviate this problem is triangu-lation. Triangulation uses a third language as a pivot through which another source-target translation system can be built. In this paper, we dynamically create multi-ple such triangulated systems and combine them using a novel approach called ensem-ble decoding. Experimental results of this approach show significant improvements in the BLEU score over the direct source-target system. Our approach also outper-forms a strong linear mixture baseline. 1