Neural Networks Classifier for Data Selection in Statistical Machine Translation

TL;DR

This paper introduces a neural network classifier for data selection in statistical machine translation, demonstrating improved translation quality over existing methods across multiple language pairs.

Contribution

The paper presents a novel neural network-based data selection method for SMT, outperforming the state-of-the-art cross-entropy approach.

Findings

Better translation quality than cross-entropy method

Consistent results across different language pairs

Empirical validation of the neural classifier's effectiveness

Abstract

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.