A Better Gauging Model for the Evaluation of Automatic Machine Translation of English – Hindi Language
Pooja Malik1, Mrudula Y2, Anurag Singh Baghel3

1Pooja Malik, School of Information, Communication and Technology, Gautam Buddha University, Greater Noida, India.
2Mrudula Y, Computer Science Department, University at Buffalo, Buffalo, New York, United States of America.
3Anurag Singh Baghel, School of Information, Communication and Technology, Gautam Buddha University, Greater Noida, India..

Manuscript received on 02 July 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 August 2019 | PP: 633-641 | Volume-8 Issue-10, August 2019 | Retrieval Number: I8965078919/2019©BEIESP | DOI: 10.35940/ijitee.I8965.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The problem of language translation has prevailed in society for so long. However, up to some extent the problem is being reduced by the online available machine translation systems like Google, Bing, Babelfish, etc. But with the emergence of these Machine Translation Systems, there arises the problem of their validation. Can we trust on such translation systems blindly? Is there no scope of improvement? Are these Machine Translation systems not prone to errors? The answer to all these questions is No. So, for this purpose, we need a mechanism that can test or assess these Machine Translation systems. In this paper, we have proposed an algorithm that will evaluate such Machine Translation systems. Our algorithm is being compared with a very well-known BLEU algorithm that works very well for non-Indian languages. The accuracy of the designed algorithm is evaluated using the standard datasets like Tides and EMILLE. 
Keywords: Automatic Machine Translation Evaluation, Automatic Machine Translation Evaluation of English-Hindi Language Pair, Automatic Evaluation Metrics for Machine Translation
Scope of the Article: Machine Learning