Quality Estimation Of Machine Translation Outputs Through Stemming

TL;DR

This paper presents an automatic ranking system for English-Hindi machine translation outputs using machine learning and morphological features, aiming to improve translation quality without human intervention.

Contribution

It introduces a novel ranking approach that leverages stemming and machine learning to evaluate translation quality for Indian languages, specifically Hindi.

Findings

The system effectively correlates with human judgments.

It reduces the need for manual evaluation.

Improves translation quality assessment accuracy.

Abstract

Machine Translation is the challenging problem for Indian languages. Every day we can see some machine translators being developed, but getting a high quality automatic translation is still a very distant dream . The correct translated sentence for Hindi language is rarely found. In this paper, we are emphasizing on English-Hindi language pair, so in order to preserve the correct MT output we present a ranking system, which employs some machine learning techniques and morphological features. In ranking no human intervention is required. We have also validated our results by comparing it with human ranking.