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Predicting Machine Translation Comprehension with a Neural Network
2016
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
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
doi:10.24297/ijct.v15i2.3980
fatcat:uglm2psv4bfj3e5aixkfw2ukq4