Data pre-processing presents the most time consuming phase in the whole process of knowledge discovery. The complexity of data pre-processing depends on the data sources used. The aim of this work is to determine to what extent it is necessary to carry out the time consuming data pre-processing in the process of discovering sequential patterns in e-documents. We used the transaction/sequence model for text representation and sequence rule analysis as a method of modelling. We compare four datasets of different quality obtained from texts and pre-processed in different ways: data with identified the paragraph sequences, data with identified the sentence sequences, data with identified the paragraph sequences without stop words and data with identified the sentence sequences without stop words. We try to assess the impact of these advanced techniques of data pre-processing on the quantity and quality of the extracted rules. The results confirm some initial assumptions, but they also show that the stop words removal has a substantial impact on the quantity and quality of extracted rules in case of paragraph sequence identification. Contrary, in case of sentence sequence identification, removing the stop words has not any significant impact on the quantity and quality of extracted rules

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.

This study focuses on the influence of quality of Machine Translation (MT) output on a translator’s performance. We analyze the translator’s effort by product analysis and process analysis. The product analysis consists of MT quality evaluation according to the Dynamic Quality Framework; using error typology and the criteria such as fluency and adequacy. We examine translator’s effort from the point of view of typing time, in the context of MT quality—focusing on error rate in language, accuracy, terminology, and style, and also in fluency and adequacy to the source text. We have found that the translator’s performance is influenced by MT quality. The typing time is very closely related to errors in language, accuracy, terminology, and style as well as to fluency and adequacy. We used the Mann-Whitney test to compare the productivity of post-editing of MT with human translation. The results of the study have shown that post-editing—compared to human translation of journalistic text from English into the inflectional Slovak language is more effective.

The rapid technologisation of translation has influenced the translation industry’s direction towards machine translation, post-editing, subtitling services and video content translation. Besides, the pandemic situation associated with COVID-19 has rapidly increased the transfer of business and education to the virtual world. This situation has motivated us not only to look for new approaches to online translator training, which requires a different method than learning foreign languages but in particular to look for new approaches to assess translator performance within online educational environments. Translation quality assessment is a key task, as the concept of quality is closely linked to the concept of optimization. Automatic metrics are very good indicators of quality, but they do not provide sufficient and detailed linguistic information about translations or post-edited machine translations. However, using their residuals, we can identify the segments with the largest distances between the post-edited machine translations and machine translations, which allow us to focus on a more detailed textual analysis of suspicious segments. We introduce a unique online teaching and learning system, which is specifically “tailored” for online translators’ training and subsequently we focus on a new approach to assess translators’ competences using evaluation techniques—the metrics of automatic evaluation and their residuals. We show that the residuals of the metrics of accuracy (BLEU_n) and error rate (PER, WER, TER, CDER, and HTER) for machine translation post-editing are valid for translator assessment. Using the residuals of the metrics of accuracy and error rate, we can identify errors in post-editing (critical, major, and minor) and subsequently utilize them in more detailed linguistic analysis.

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.