“Why would they think this?”Īfter a few hours, even the same r/the_donald post was amended to answer that question. Users should refer to the original published version of the material for the full abstract.“This is insane,” Yale University Islam scholar and Arabic speaker Andrew March told The Daily Beast. No warranty is given about the accuracy of the copy. However, users may print, download, or email articles for individual use. Copyright of Language in India is the property of Language in India and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission.The strategy for evaluation is to evaluate the Urdu output text based on the five-point scale with scores that range from 0-4 where 0 refers to incomprehensible or ungrammatical, 1 = little meaning or disfluent, 2 = neutrality, 3 = comprehensible or grammatical and 4 suggests flawless in both cases. In addition, all the erroneous entities have been analyzed through computational typology. So far as WER and SER are concerned, NMT has 32.58% and 28% respectively.
On the contrary, NMT module has Kappa scores of 0.61 and 1 on comprehensibility and grammaticality respectively. Furthermore, the system has also been quantitatively evaluated on the basis of word error rate (21.11%) and sentence error rate (72.39%). The Kappa scores of PBSMT for comprehensibility and grammaticality are 0.24 and 0.22 respectively which is indicative of the fact that on both counts the scores are not up to the mark.
The scores are calculated by the Fleiss' Kappa statistical measure with regard to comprehensibility and grammaticality on the basis of which error analysis and suggestions have been provided for improvement. In order to evaluate the output text in a qualitative manner, the Inter-translator Agreement (IA) of three human translators has been considered with their scores on a five-point scale. In this study, a model corpus set of 100 English sentences has been applied out of 1k cross-domain data considering various types of verbs as input text to evaluate the output of the online systems in Urdu. This system is popularly known as Rosetta, formerly governed by Phrase-based approach and is presently governed by the neural module of source and target languages. Abstract: The paper demonstrates the qualitative evaluation of the English to Urdu Machine Translation Systems, namely PBSMT and NMT hosted on Google's Translate.
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