Neural Networks Classifier for Data Selection in Statistical Machine Translation
This work addresses data selection for statistical machine translation, which is an incremental improvement over existing methods.
The paper tackled the data selection problem in statistical machine translation by proposing a neural network classifier, resulting in better translation quality compared to a state-of-the-art cross-entropy method, with empirical results showing coherence across different language pairs.
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.