Quality Estimation of MT-Engine Output Using Language Models for Post-editing and Their Comparative Study

  • Kuldeep Kumar Yogi
  • Nishith Joshi
  • Chandra Kumar Jha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 340)


Machine Translation (MT) systems do not have real-world knowledge or contextual awareness. MT errors are possible at any level: lexical, grammatical, syntactic, etc., MT systems give 10–70 % accurate output, so human post-editing(HPE) is required for final output. But HPE is very expensive and slow, if we can filter out good translations out of all translations, those can make correct via miner edits then our HPE would be fast and less expensive. We can estimate good quality of a sentence using language model (LM). There are different LMs available. We showed in our experiment that Kneser-Ney smoothing LM is the right choice for measuring MT-Engine-output’s quality for the post-editing.


Machine translation Smoothing Postediting Quality-estimation Good translation MT-engine 


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Copyright information

© Springer India 2015

Authors and Affiliations

  • Kuldeep Kumar Yogi
    • 1
  • Nishith Joshi
    • 1
  • Chandra Kumar Jha
    • 1
  1. 1.Banasthali UniversityRajasthanIndia

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