Method of Selecting Training Data to Build a Compact and Efficient Translation Model

Target task matched parallel corpora are re-quired for statistical translation model train-ing. However, training corpora sometimes include both target task matched and un-matched sentences. In such a case, train-ing set selection can reduce the size of the translation model. In this paper, we propose a training set selection method for transla-tion model training using linear translation model interpolation and a language model technique. According to the experimental results, the proposed method reduces the translation model size by 50 % and improves BLEU score by 1.76 % in comparison with a baseline training corpus usage.