# Evaluation of English-Slovak Neural and Statistical Machine Translation
## 5 Discussion
In such cases, MT is not able to choose a meaning that is adequate for a particular context from a large range of homonymous and polysemantic words. In the GT_SMT, the phrase \"how much\" can be translated as \"kofko\" as well as \"nakoIko\". Such errors can be revised by a human post-editor. The GT_NMT was adequate and fluent. The BLEU_4 metric has a specific nature; it is a match of four consecutive tokens, regardless of whether they are words or punctuation. The following text can serve as an example:  
(4)  
Source: He said they would facilitate \"speedier, clearer investigations and stricter prison sentences\" and would help the authorities understand Isis's \"underlying structures\". GT_SMT: Hovoril, ze by to ufahiclo \"rychlejise, cistejsie vystrovanie a prisnejsie tresty odnatia slobody\" a bol by pomohla organom pochopit' ISIS je \"zakladnej struktury\". (Back translation: He said they would facilitate \"speedier, clearer investigations and stricter prison sentences\" and would help the authorities understand Isis is \"underlying structures\".) GT_NMT: Povedal, ze by ufahicli,rychlejise a jasnejsie vystrovanie a prisnejsie tresty odnatia slobody\"a pomozu uradom pochopit,zakladne struktury\"Isis. (Back translation: He said they would facilitate \"speedier, clearer investigations and stricter prison sentences\" and would help the authorities understand Isis's \"underlying structures\".) Reference: Povedal, ze budu presadzovat,rychlejise, jasnejsie vystrovania a prisnejsie tresty odnatia slobody\", a pomozu uradom pochopit,zakladne struktury Isis\". In GT_SMT, we can observe a literal translation, especially in the possessive form \"Isis's\", which was confusingly translated as a short form of the verb \"is/has\". Our research results also pointed to the opposite phenomenon, i.e., to texts in which neural machine translation simultaneously achieved a lower score for all BLEU_n metrics than its predecessor (GT_SMT)--specifically, in texts that contained sentences with extra words, for example:  
(5)  
Source: The search for suspects continues. GT_SMT: Patranie po podozrivych pokracuje. GT_NMT: Patranie po podozrivych osobach pokracuje. Reference: Patranie po podozrivych pokracuje. The outputs of both GT_SMT and GT_NMT were adequate, correct, and fluent; GT_NMT offered the explication \"osobach\" (persons), which was not required. In the case of the EC's tool, the results were similar, but not as significantly different as in the case of GT. In six texts, the neural machine translation achieved a better score for the BLEU_1 metric than its predecessor. This was mainly an increase in the quality of translation, i.e., in the adequacy of the transfer of the source text into the target language:  
(6)  
Source: Among their number were Belgian students, French schoolchildren and British lawyers. mt@ec_SMT: Spomedzi nich boli belgicke, francuzske a britske studentov, advokatov. (Back translation: Among their number were Belgian, French and British students, lawyers.) eTranslation_NMT: Medzi ich pocet boli belgicki studenti, francuzski ziaci a britski pravnici. (Back translation: Among their number were Belgian students, French pupils and British lawyers. )Reference: Nachadzaju sa medzi nimi belgicki studenti, francuzski skolaci a britski pravnici. In GT_SMT, we can observe the issues of agreement in the case and gender (\"belgicke, francuzske a britske studentov\") and the issue of the adequate transfer of the word \"among\" (in this context, \"medzi nimi\" is a better option than \"spomedzi nich\"). Moreover, some lexemes in the syntagms \"Belgian students, French schoolchildren and British lawyers\" were omitted; the syntagms were broken, and they were translated incorrectly. Similar results were shown for the BLEU_2 metric. The neural machine translation achieved a better translation quality due to the reference.