Quality Estimation of English-Hindi Outputs using Naive Bayes Classifier
This work addresses quality estimation for machine translation in the English-Hindi language pair, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of estimating the quality of English-Hindi machine translation outputs by using a Naive Bayes classifier with features extracted from input sentences, resulting in a method that assigns class labels to test data based on likelihood scores.
In this paper we present an approach for estimating the quality of machine translation system. There are various methods for estimating the quality of output sentences, but in this paper we focus on Naïve Bayes classifier to build model using features which are extracted from the input sentences. These features are used for finding the likelihood of each of the sentences of the training data which are then further used for determining the scores of the test data. On the basis of these scores we determine the class labels of the test data.