We improve our RTM models<cite> (Biçici and Way, 2014</cite> ): • by using improved ParFDA instance selection model allowing better language models (LM) in which similarity judgments are made to be built with improved optimization and selection of the LM data, • by selecting TreeF features over source and translation data jointly instead of taking their intersection, • with extended learning models including bayesian ridge regression (Tan et al., 2015) , which did not obtain better performance than support vector regression in training results (Section 2.2).