TY - JOUR AU - Aiken, Milam AU - Posey, Jamison AU - Garner, Bart AU - Reithel, Brian PY - 2015/12/08 Y2 - 2024/03/29 TI - Predicting Machine Translation Comprehension with a Neural Network JF - INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY JA - IJCT VL - 15 IS - 2 SE - Research Articles DO - 10.24297/ijct.v15i2.3980 UR - https://rajpub.com/index.php/ijct/article/view/3980 SP - 6546-6554 AB - 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. ER -