# Evaluation of English-Slovak Neural and Statistical Machine Translation
## 2 Related Work
Statistically significant differences were identified for all evaluation metrics between the phrase-based SMT and NMT systems. In addition, due to the unavailability of a gold standard for evaluation of terminology translations, the authors created an approach that semi-automatically created a gold-standard test set from an English-Hindi parallel corpus. The sentences for the test were translated using their phrase-based SMT and NMT systems. [PERSON] et al. [23] focused on a sentiment analysis approach based on a novel hybrid framework for concept-level analysis. It integrated linguistic rules and deep learning to optimize polarity detection. The authors compared the novel framework with state-of-the-art approaches, such as support vector machine, logistic regression, and deep neural network classifiers. A sentiment analysis for Persian in which the current approaches were focused on word co-occurrence frequencies was examined. The proposed hybrid framework achieved the best performance in comparison to the other sentiment analysis methods mentioned. [PERSON] [24] compared the two most popular machine translation systems, Google Translate and the Microsoft Bing translator. Both systems used statistical machine translation. The examined language pair was English and Arabic. The dataset consisted of sentences extracted from a political news agency. A comparison of the MT outputs was conducted using the BLEU automatic evaluation metric. The results were in favor of Google Translate; Bing generated different sentences. The limitation of the experiment was that its corpus consisted of 25 sentences. [PERSON] [25] compared two machine translation systems: Google Translate and the Microsoft Bing translator. The dataset consisted of journalistic texts written in Arabic, and the target language was English for the input texts. Both MT systems used NMT, and they were compared using linguistic error analysis and error classification. The author focused on three main categories for the comparison: orthography, lexis, and grammar. The results showed similar results for both MT systems in orthography and grammar (approximately 92%). The difference was found in the case of lexis, where Google achieved better results than Bing. The limitation of the experiment was its small dataset. [PERSON] et al. [26] focused on a comparison of three MT systems: Google Translate, Matecat, and Thot. The article aimed to support the creation of Dutch interface terminologies for the usage of SNOMED CT using MT. SNOMED CT contains more than 55,000 procedures with English descriptions for which human translation would require a lot of time and resources. The MT system Matecat [27] is specific with its translation memory and offers the option of selecting the field of translation. Thot [28] is an MT system based on phrases; it was trained for the experiment using Dutch and English noun phrases. The outputs of the examined MT systems were evaluated by two reviewers. The translations were considered acceptable by the reviewers when they covered the meanings of the English terms. The results of the experiment showed that the quality of all three MT systems was not good enough for use in clinical practice, and none of them translated the terms according to the translation rules of SNOMED CT. The worst results were achieved for Thot, and the results for Google Translate and Matecat were similar. [PERSON] and [PERSON] [29] focused on the issue of out-of-vocabulary words. The authors integrated morphosyntactic features into NMT models and dealt with long sentences that create issues for NMT systems in the English-Arabic language pair. The experiment was realized under low- and high-resource conditions; novel segmentation techniques were proposed to overcome the issues with long sentences. Under low-resource conditions, the BLEU scores decreased according to the sentence size. This indicated the need to shorten the sentences before translation. [PERSON] and [PERSON] [30] evaluated SMT and NMT models trained on a domain-specific English-Polish corpus of medium size.