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This paper considers the problem for estimating the quality of machinetranslation outputs which are independent of human intervention and aregenerally addressed using machine learning techniques.There are variousmeasures through which a machine learns translations quality. AutomaticEvaluation metrics produce good co-relation at corpus level but cannot producethe same results at the same segment or sentence level. In this paper 16features are extracted from the input sentences and their translations and aquality score is obtained based on Bayesian inference produced from trainingdata.

Analysing Quality of English-Hindi Machine Translation Engine Outputs Using Bayesian Classification