# Evaluation of English-Slovak Neural and Statistical Machine Translation
## 1 Introduction  
Machine translation (MT) is a sub-field of computational linguistics that primarily focuses on automatic translation from one natural language into another natural language without any intervention [1]. Neural machine translation (NMT) is an approach that is used by many online translation services, such as Google Translate, Bing, Systran, and eTranslation. NMT works on the principle of predicting the probability of a word sequence. Using sequence-to-sequence models, NMT took a huge leaf forward in accuracy [1]. It uses a deep neural network to process huge amounts of data, and is primarily dependent on training data, from which it learns. If there is a substantial dataset for training the model, then NMT can process any language pair, including languages that are difficult to understand. NMT allows the processing of text in various language styles, such as formal, medical, financial, and journalistic. Neural networks are flexible and can be adjusted to many needs. This is ensured by several parameters with weights and interconnections between individual nodes of the neural network. In the field of natural language processing (NLP), this indicates the ability to create millions of language pairs. Word representation in NMT is simpler than in phrase-based MT systems. Each word is encoded into a vector of a unique size and direction. The language and translation model is replaced by a single-sequence model, which gradually forms word by word. To predict the probability of a word sequence, it is necessary to use a neural network that can remember the previous sequence of words in asentence. Feedforward neural networks (FNNs) process inputs independently from the rest of the sentence. Recurrent neural networks (RNNs) are ideal for this problem. RNNs are used for both the encoder and decoder, which are the basis for machine translation using NMT. The encoder is used to process the source sentence, which is read and encoded into a vector that captures the \"meaning\" of the input sequence. The decoder processes this vector to produce an output sequence in the target language. Google Translate (neural machine translation) is one of the most widely used online translator systems today, thanks to its more natural translations and its ability to process so-called \"zero-frame translations\". Natural translation provides training in algorithms on large amounts of data. The ability of the so-called \"zero-range translation\", i.e., direct translation from the source language to the target language without the need for an intermediate step-translation into English, is an improvement over the previous version of Google Translate (statistical machine translation or rule-based machine translation). American linguist [PERSON] [2] argues that language learning is an innate ability that is typical only for humans due to the neural structure of the human brain, and that the environment only shapes the contours of this network into a particular language. Thanks to the so-called \"universal grammar\", i.e., a set of syntactic rules and principles that are the same for all languages, it is possible to learn to create sentences and interpret them in any language. [PERSON] [3] summarized the arguments for and against the usage of universal grammar in his article. [PERSON]'s main argument for the existence of universal grammar concludes that even though children do not have the required linguistic input to \"learn\" grammatical structures, they still make virtually no mistakes. The use of universal grammar could probably lead to more adequate outputs of machine translations. It would be most suitable for low-resource languages and languages with inflected morphologies and loose word orders. Despite that, the debate about universal grammar is still ongoing, with many supporters on both sides. The need to evaluate MT systems through their outputs (products) is not a current phenomenon; it has existed as long as machine translation itself [4]. In general, we can approach quality assessment manually or automatically.