Classifier-Based Text Simplification for Improved Machine Translation

TL;DR

This paper introduces a classifier-based text simplification approach using SVM and Naive Bayes to enhance English-Hindi machine translation quality, aiming to improve translation accuracy and coverage.

Contribution

It presents a novel text simplification model leveraging classifiers specifically designed for improving machine translation between English and Hindi.

Findings

Support Vector Machine outperforms Naive Bayes in simplification tasks.

Simplification improves translation accuracy and coverage.

The model demonstrates effective classifier performance in text simplification.

Abstract

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 Na\"ive Bayes Classifier to develop this model. We have also evaluated the performance of these classifiers.