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Machine Translation Evaluation: Manual Versus Automatic—A Comparative Study

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Data Engineering and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1079))

Abstract

The quality of machine translation (MT) is best judged by humans well versed in both source and target languages. However, automatic techniques are often used as these are much faster, cheaper and language independent. The goal of this paper is to check for correlation between manual and automatic evaluation, specifically in the context of Indian languages. To the extent automatic evaluation methods correlate with the manual evaluations, we can get the best of both worlds. In this paper, we perform a comparative study of automatic evaluation metrics—BLEU, NIST, METEOR, TER and WER, against the manual evaluation metric (adequacy), for English-Hindi translation. We also attempt to estimate the manual evaluation score of a given MT output from its automatic evaluation score. The data for the study was sourced from the Workshop on Statistical Machine Translation WMT14.

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Notes

  1. 1.

    https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/mteval-v13a.pl.

  2. 2.

    http://www.cs.cmu.edu/~alavie/METEOR/.

  3. 3.

    http://www.cs.umd.edu/~snover/tercom/.

  4. 4.

    We used the tconfint_mean function available in the statsmodels package in Python.

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Authors and Affiliations

  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

    Kaushal Kumar Maurya, Renjith P. Ravindran, Ch Ram Anirudh & Kavi Narayana Murthy

Authors
  1. Kaushal Kumar Maurya
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  2. Renjith P. Ravindran
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  3. Ch Ram Anirudh
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  4. Kavi Narayana Murthy
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Corresponding author

Correspondence to Kaushal Kumar Maurya .

Editor information

Editors and Affiliations

  1. Professor & Head, Department of Computer Science & Engineering, CMR Technical Campus, Hyderabad, India

    K. Srujan Raju

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Roman Senkerik

  3. Stanley College of Engineering and Technology, Hyderabad, Telangana, India

    Satya Prasad Lanka

  4. Department of EEE, Stanley College of Engineering and Technology, Hyderabad, Telangana, India

    V. Rajagopal

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Maurya, K.K., Ravindran, R.P., Anirudh, C.R., Murthy, K.N. (2020). Machine Translation Evaluation: Manual Versus Automatic—A Comparative Study. In: Raju, K.S., Senkerik, R., Lanka, S.P., Rajagopal, V. (eds) Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-15-1097-7_45

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