Method of Selecting Training Data to Build a Compact and Efficient Translation Model
Keiji Yasuda, Ruiqiang Zhang, Hirofumi Yamamoto, Eiichiro Sumita · 2008
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. 1