Finally, inspired by study in summarization we propose TriS, a novel framework to simplify source sentences before translating them. We build a statistical sentence simplification system with log-linear models. In contrast to state-of-the-art methods that drive sentence simplification process by hand-written linguistic rules, our method used a margin-based discriminative learning algorithm operates on a feature set. The feature set is defined on statistics of dependency structures as well as surface form and syntactic structures of sentences. A stack decoding algorithm is developed in order to efficiently generate and search simplification hypotheses. Experimental results show that the simplified text produced by the proposed system reduces 1.7 Flesch-Kincaid grade level when compared with the original text. We show that a comparison of a state-of-the-art rule-based system to the proposed system demonstrates an improvement of 0.2, 0.6, and 4.5 points in ROUGE-2, ROUGE-4, and AveF 10, respectively. We present subjective evaluations of the simplified translation quality for an English-German MT system.
