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.
@article{arxiv.1612.05555,
title = {Neural Networks Classifier for Data Selection in Statistical Machine Translation},
author = {Álvaro Peris and Mara Chinea-Rios and Francisco Casacuberta},
journal= {arXiv preprint arXiv:1612.05555},
year = {2016}
}