# Evaluation of English-Slovak Neural and Statistical Machine Translation
## 2 Related Work  
NMT is a model based on an encoder-decoder framework that includes two neural networks for an encoder and a decoder [8; 9]. The encoder maps the source text input token into a vector and then encodes the sequence of vectors into semantically distributed semantic representations that serve for the decoder to generate the target language sentence [10]. Based on a field review done by [10], an encoder-decoder based on a recurrent neural network [8; 11] was changed through the convolutional neural network [9] into a self-attention-based neural network transformer [12]. This transformer is state of the art in terms of both quality and efficiency. Within the transformer, the encoder consists of \(N\) identical layers, which are composed of two sublayers: a self-attention sublayer and a feedforward sublayer. The output is the source-side semantic representation. The decoder has also \(N\) identical layers composed of three sublayers [10]:  
* Masked self-attention mechanism summarizing the partial prediction history;
* Encoder-decoder attention sublayer determining the dynamic source-side contexts for current prediction;
* Feedforward sublayer. In this Section, we focus on papers dealing with a linguistic evaluation of the MT outputs of various MT systems. [PERSON] et al. [13] analyzed the performance of the statistical and neural approaches to machine translation. They compared phrase-based and neural-based MT systems and their combination. The examined language pairs were Czech-Polish and Spanish-Portuguese, and the authors used a large sample of parallel training data (they used a monolingual corpus and a pseudo-corpus). The authors applied back translation into their MT system and observed the results using the BLEU (Bilingual Evaluation Understudy) score [14]. The results showed that for the Czech-Polish language pair, the BLEU score was relatively low, which was explained by the language distance. [PERSON] et al. [15] focused on examining the applicability of NMT in the field of literary translation. The authors studied the outputs of the Google Translate and DeepL MT systems. The dataset consisted of four English classic novels and their translations into Dutch. The authors compared the human translations with two MT outputs. The error analysis was done using the SCATE error taxonomy [16; 17], which is a hierarchical error taxonomy based on the distinction between accuracy and fluency errors. The results showed that most of the sentences contained errors. More fluency errors were identified than accuracy errors. The literary NMT had more difficulty in producing correct sentences than accurately representing what was being said in the source text. [PERSON] et al. [18] proposed a robust unsupervised neural machine translation with adversarial attack and regularization on representations in their paper. The authors encouraged the encoder to map sentences of the same semantics in different languages to similar representations in order to be more robust to synthesized or noisy sentences. The authors used the BLEU score to evaluate both models for the English-French language pair, which was used as the validation set. The results showed that the model with epsilon correction had a more stable training curve and a slightly better translation score. [PERSON] et al. [19] focused on the investigation of term translation in NMT and phrase-based SMT. They created their own phrase-based SMT systems using the Moses toolkit. On the other hand, the NMT systems were created using the MarianNMT [20] toolkit. The parallel corpus was the English-Hindi language pair, and it served to create the baseline transformer models. An evaluation of the systems was conducted using the following automatic evaluation metrics: BLEU, METEOR [21], and TER (Translation Error Rate) [22]. The results show that the English to Hindi translation produced reasonable BLEU scores for the free-word-order language of Hindi, with a better score for the NMT. In the case of Hindi to English translation, moderate BLEU scores were produced.