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

Target task matched parallel corpora are required for statistical translation model training. However, training corpora sometimes include both target task matched and unmatched sentences. In such a case, training set selection can reduce the size of the translation model. In this paper, we propose a training set selection method for translation 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.