Predicting Machine Translation Comprehension with a Neural Network


  • Milam Aiken University of Mississippi, School of Business Administration, University, MS 38677
  • Jamison Posey University of Mississippi, School of Business Administration, University, MS 38677
  • Bart Garner University of Mississippi, School of Business Administration, University, MS 38677
  • Brian Reithel University of Mississippi, School of Business Administration, University, MS 38677



Comprehension, Neural network, Machine translation


Comprehension of natural language translations is dependent upon several factors including textual variables (grammatical, spelling, and word choice errors, sentence complexity, etc.) and human variables (language fluency, topic knowledge, motivation, dyslexia, etc.). An individual reader’s understanding of machine-generated translations can vary widely because of the lower accuracy usually associated with this technology. Prior studies have had mixed results in predicting which variables have the greatest influence on translation comprehension. In the current study, we employ an artificial neural network to analyze survey responses and reading test scores, resulting in a significantly correlated forecast of reading comprehension. Thus, we are able to offer better predictions to identify which readers might have a better grasp of content from garbled translations.


Download data is not yet available.


[1] Aiken, M. and Balan, S. (2011). An analysis of Google Translate accuracy. Translation Journal. 16(2).
[2] Aiken, M., Balan, S., Vanjani, M., and Garner, B. (2010). The effect of comment errors in multilingual electronic
meetings. Communications of the International Information Management Association. 10(4), 49-60.
[3] Aiken, M., Park, M., and Lindblom, T. (2013). Language fluency as a factor in machine translation comprehension.
International Journal of Computers and Technology. 10(2), 1349-1355.
[4] Aiken, M., Wang, J., Gu, L., and Paolillo, J.(2011). An exploratory study of how technology supports communication in
multilingual groups. International Journal of e-Collaboration, 7(1), 17-29.
[5] Chatal, R. (2001). Diagnostic and instructional uses of the Cloze procedure. The Nera Journal, 37(1), 3-6.
[6] Dostál, P. (2013). The use of soft computing methods for forecasting in business, their applications in practice. In
Nostradamus: Modern Methods of Prediction, Modeling and Analysis of Nonlinear Systems (pp. 49-60). Springer
Berlin Heidelberg.
[7] ElShiekh, A. (2012). Google translate service: Transfer of meaning, distortion or simply a new creation? An
investigation into the translation process & problems at google. English Language and Literature Studies, 2(1), 56.
[8] García, I. (2010). Is machine translation ready yet? Target, 22(1), 7-21.
[9] Maren, A., Harston, C., and Pap, R. (2014). Handbook of neural computing applications. Academic Press.
[10] McVay, J. and Kane, M. (2012). Why does working memory capacity predict variation in reading comprehension? On
the influence of mind wandering and executive attention. Journal of Experimental Psychology, 141(2), 302-320.
[11] Pepper, W., Aiken, M., and Garner, B. (2011). Usefulness and usability of a multilingual electronic meeting system.
Global Journal of Computer Science and Technology, 11(10), 35-40.
[12] Posey, J. and Aiken, M. (2015). Large-scale, distributed, multilingual, electronic meetings: A pilot study of usability
and comprehension. International Journal of Computers and Technology, 14(3), 5578-5585.
[13] Schmitt, N., Jiang, X., and Grabe, W. (2011). The percentage of words known in a text and reading comprehension.
The Modern Language Journal, 95(1), 26-43.
[14] Sénéchal, M. (2006). Testing the home literacy model: Parent involvement in kindergarten is differentially related to
grade 4 reading comprehension, fluency, spelling, and reading for pleasure. Scientific Studies of Reading, 10(1), 59-
[15] Vanjani, M., Aiken, M., and Park, M. (2015). A study of factor sinfluencing machine translation comprehension.
Quarterly Review of Business Disciplines, 2(2), 121-132.
[16] Wade, R. (2011). Try Google Translate to overcome language barriers. BMJ, 343.
[17] Williams, R., Ari, O., and Santamaria, C. (2011). Measuring college students' reading comprehension ability using
Cloze tests. Journal of Research in Reading, 34(2), 215-231.




How to Cite

Aiken, M., Posey, J., Garner, B., & Reithel, B. (2015). Predicting Machine Translation Comprehension with a Neural Network. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 15(2), 6546-6554.



Research Articles