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.
REF selected the training set for translation model training using linear translation model interpolation and a language model technique.
Method of Selecting Training Data to Build a Compact and Efficient Translation Model
Proceedings of the Third International Joint Conference on Natural Language Processing
Computer Science