  We address the data selection problem in statistical machine translation
(SMT) as a classification task. The new data selection method is based on a
neural network classifier. We present a new method description and empirical
results proving that our data selection method provides better translation
quality, compared to a state-of-the-art method (i.e., Cross entropy). Moreover,
the empirical results reported are coherent across different language pairs.
