# Evaluation of English-Slovak Neural and Statistical Machine Translation
## 5 Discussion  
As the results showed, in the case of Google Translate and based on the BLEU_1 metric, neural machine translation proved to be significantly better for six texts compared to its predecessor, statistical machine translation. In the more detailed linguistic analysis, it was mainly about increasing the accuracy of the transfer of the meaning of the source text and reducing the literal translations and untranslated words. Examples of sentences are:  
(1)  
Source: He is believed to have stayed for a time in Belgium but has never been found. GT_SMT: On je veril k zdziavala na nejaky cas v Belgicku, ale nebolo nikdy najdene. GT_NMT: Predpoklada sa, ze nejaky cas zostal v Belgicku, ale nikdy ho nenasli. Reference: Predpoklada sa, ze isty cas v Belgicku zostal, no policia ho nikdy nevpqrtala. In GT_SMT, the English structure \"he is believed to have stayed\" was translated literally. The passive form \"is believed\" was not recognized; the past participtic form was translated as the past-tense form, and the verb \"is\" (representing the present tense) was followed by a verb in the past tense (which sounds illogical). The issue of literal machine translation is discussed in [41]. The GT_NMT was correct and fluent, although the reference explicitly suggests the noun \"policia\" (the police). (2)  
Source: The flexible partner will eventually adapt their entire life around the inflexible partner's insistences. GT_SMT: Flexible partner bude nakoniec prisposobit' cely soj zivot okolo insistences nepoddajna partnera. GT_NMT: Flexiblemj partner nakoniec prisposobi cely soj zivot poziadavkam nepruzneho partnera. Reference: Nakoniec flexibilmj partner prisposobi cely soj zivot poziadavkam neflexibilneb partnera. We can observe some mistakes that are typical for SMT outputs: There is a low agreement between the words \"flexibilne\" and \"partner\" (the correct form is \"flexibiln-y partner\"). The form \"flexibiln-e\" refers to the neuter gender, and the noun \"partner\" refers to the male gender. The same issue is detected in the form \"nepoddajna partnera\". GT_SMT suggests using the form \"nepoddajn-a\", which is in the female gender, and the noun \"partner-a\", which is in the male gender. In addition to the discrepancy in the agreement in the gender, we can see the discrepancy in the category of the case (the form \"_nepoddajn-a_\" is in the nominative case, and \"partner-a\" is in the genitive or accusative case). The agreement in the prediction--namely in the predicative and agreement categories--can be crucial in the comprehensibility of a text [42]. The issues of untranslated lexemes (e.g., \"insistences\" in this segment) are typical for SMTs; omitted lexemes are more common for NMTs [41]. The GT_NMT was adequate and fluent. In comparison with the reference, there was just a slight adjustment in the word order, which was not necessary. However, an interesting finding was that only one text (ID_183) had a significantly more accurate translation in terms of all four metrics (BLEU_1 to BLEU_4). Regarding the BLEU_2 metric, in only four texts, NMT performed better than SMT. An interesting finding is that there was also a text (ID_240) in which the same quality improvement was proven by BLEU_3 (increase in the adequacy and fluency). This text contained sentences such as:  
(3)  
Source: How much should we fear the rise of artificial intelligence? GT_SMT: Kof'ko by sme mali bat' vzostup umelej inteligencie? GT_NMT: Nakof'ko by sme sa mali bat' narastu umelej inteligencie? Reference: Nakof'ko by sme sa mali obavat' vzostup umelej inteligencie?The GT_SMT points out the issue of the incorrect transfer of words. The errors occur due to the different typological characters of English and Slovak. This can be explained by the fact that homonymy and polysemy are typical for languages with poor derivation (e.g., English). Slovak has a richer derivation of words and a lower number of polysemous and homonymous words [43].