Quality Estimation Of Machine Translation Outputs Through Stemming
This addresses the challenge of improving translation quality for Indian languages, specifically English-Hindi, but appears incremental as it builds on existing ranking and feature-based methods.
The paper tackles the problem of low-quality English-Hindi machine translation by developing a ranking system that uses machine learning and morphological features to preserve correct outputs, achieving validation through comparison with human rankings.
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.