This study is focused on the comparison of phrase-based statistical machine translation (SMT) systems and neural machine translation (NMT) systems using automatic metrics for translation quality evaluation for the language pair of English and Slovak. As the statistical approach is the predecessor of neural machine translation, it was assumed that the neural network approach would generate results with a better quality. An experiment was performed using residuals to compare the scores of automatic metrics of the accuracy (BLEU_n) of the statistical machine translation with those of the neural machine translation. The results showed that the assumption of better neural machine translation quality regardless of the system used was confirmed. There were statistically significant differences between the SMT and NMT in favor of the NMT based on all BLEU_n scores. The neural machine translation achieved a better quality of translation of journalistic texts from English into Slovak, regardless of if it was a system trained on general texts, such as Google Translate, or specific ones, such as the European Commission’s (EC’s) tool, which was trained on a specific-domain.
The study describes an experiment with different estimations of reliability. Reliability reflects the technical quality of the measurement procedure such as an automatic evaluation of Machine Translation (MT). Reliability is an indicator of accuracy, the reliability of measuring, in our case, measuring the accuracy and error rate of MT output based on automatic metrics (precision, recall, f-measure, Bleu-n, WER, PER, and CDER). The experiment showed metrics (Bleu-4 and WER) that reduce the overall reliability of the automatic evaluation of accuracy and error rate using entropy. Based on the results we can say, that the use of entropy for the estimation of reliability brings more accurate results than conventional estimations of reliability (Cronbach's alpha and correlation). MT evaluation, based on n-grams or edit distance, using entropy could offer a new view on lexicon-based metrics in comparison to commonly used ones.
The research was aimed at finding the measure of influence of cognitive-individual variables (Need for Structure, Ability to Achieve Cognitive Structure, Self-Esteem, Cognitive Style 'Category Width'), linguistic variables (Verbal Intelligence, Morphology Score), and demographic variables (Study-year, Grade, Living abroad) on syntactic abilities of students studying English language and culture at the Constantine the Philosopher University in Nitra. Subsequently, we investigated the relation between syntactic ability and chosen variables. We used the following research methods:
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