Classifier-Based Text Simplification for Improved Machine Translation
This work addresses translation quality for English-Hindi language pairs, but appears incremental as it applies existing classifiers without novel methodological breakthroughs.
The paper tackles the problem of improving machine translation quality by developing a classifier-based text simplification model for English-Hindi systems, using support vector machines and Naïve Bayes classifiers, but does not report concrete performance numbers or results.
Machine Translation is one of the research fields of Computational Linguistics. The objective of many MT Researchers is to develop an MT System that produce good quality and high accuracy output translations and which also covers maximum language pairs. As internet and Globalization is increasing day by day, we need a way that improves the quality of translation. For this reason, we have developed a Classifier based Text Simplification Model for English-Hindi Machine Translation Systems. We have used support vector machines and Naïve Bayes Classifier to develop this model. We have also evaluated the performance of these classifiers.