Using Example-Based MT to Support Statistical MT when Translating Homogeneous Data in a Resource-Poor Setting

In this paper, we address the issue of applying example-based machine translation (EBMT) methods to overcome some of the difficulties encountered with statistical machine translation (SMT) techniques. We adopt two different EBMT approaches and present an approach to augment output quality by strategically combining both EBMT approaches with the SMT system to handle issues arising from the use of SMT. We use these approaches for English to Turkish translation using the IWSLT09 dataset. Improved evaluation scores (4% relative BLEU improvement) were achieved when EBMT was used to translate sentences for which SMT failed to produce an adequate translation.