# Evaluation of English-Slovak Neural and Statistical Machine Translation
## 6 Conclusions  
Our research aimed to establish whether machine translation based on neural networks achieves a higher quality than its predecessor, statistical machine translation, in terms of translation accuracy.  
We can summarize the results of our research into two interesting contributions. The first contribution is that neural machine translation achieves a better quality of translation of journalistic texts from English into Slovak, whether it uses a system trained on general texts (Google Translate) or a specific one that is trained on a specific domain (the EC's tool). This study--a linguistic evaluation of MT output--is unique in the examined direction of translation. Although there are studies in which the Slovak language was used, these studies were focused on training MT systems using several languages, e.g., for training massively multilingual NMT models or adapting neural machine translation systems to new, low-resource languages [45; 46]. Machine translation into Slovak has seldom been explored in contrast to Czech, which is genetically very close to Slovak, and for which several studies focusing on evaluating the quality of machine translation exist [47; 48].  
The second contribution is that, for machine translation of journalistic texts (mostly of the news genre) from English into Slovak, it is better to use a general MT system (GT) than a specialized one, even though is the texts are of the news genre. Of all the MT systems examined, GT_NMT achieved the highest translation quality due to its accuracy, as measured using the BLEU_n metrics and the reference. There was no significant difference in translation quality between the GT_SMT statistical machine translation and the eTranslation_NMT neural machine translation due to BLEU_4.  
Our results correspond to the findings of [1] which show that it is possible to obtain 70-80% accuracy in machine translation using artificial intelligence (multilingual NMT models); however, the rest is still a task for human translation.  
The future direction of our research will consist of expanding the examined texts and using other automatic metrics for evaluating the quality of translation, such as HTER (Human-targeted Translation Error Rate), which is based on edit distance.  
The following are available online at [[http://dx.doi.org/10.17632/r6f4](http://dx.doi.org/10.17632/r6f4)]([http://dx.doi.org/10.17632/r6f4](http://dx.doi.org/10.17632/r6f4)) 4z6 ycf.1 (accessed on 28.1.2021): Dataset S1: data.txt.  
Conceptualization, L.B. and M.M.; methodology, L.B.; validation, D.M. and M.M.; formal analysis, D.M. and M.M.; investigation, L.B.; resources, L.B. and L.B.; data curation, L.B.; writing--original draft preparation, L.B. and L.B.; writing--review and editing, D.M. and M.M.;visualization, M.M.; supervision, D.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Scientific Grant Agency of the Ministry of Education of the Slovak Republic (ME SR) and the Slovak Academy of Sciences (SAS) under contract No. VEGA-1/0809/18, as well as by the scientific research project of the Czech Sciences Foundation Grant No.: 19-15498S and by the Slovak Research and Development Agency under contract no. APVV-18-0473.  
The data presented in this study are openly available in [Mendeley Data] at [doi:10.17632/r6f44z6 ycf.1], reference number [35].  
The authors declare no conflict of interest.