## 5 Related Works
The sentences were manually translated. Using the corpi, the training and test datasets were created using the following steps: first, the number of words in sentences was counted, and if the number exceeded 90, the sentence was rejected. Next, if the ratio of the number of words in sentences from both the reference text and the translated source text did not fall between 0.5 and 2, inclusively, the sentence would also be rejected [37]. To evaluate the RBMT system combined with the statistical post-editor (SPE), the authors propose a new metric of evaluation: an n-gram-based NMG measure. NMG, or normalized mean grams, which counts the total number of words within the \"longest word sequence matches between the test sentence and the target language reference corpus\" [37]. Their results concluded that for patent translation where sentences are long and complex, the RBMT provided an advantage for structural transfer [37]. Additionally, since patents are made up of very technical terms, the SPE provided improved lexical transfer [37]. Before NMT systems became popularized, SMT models were widely used for translation purposes. SMT systems use a phrase-based approach, which allows them to reduce the restrictions of RBMT's word-based approach by translating sequences of words at a time. NMT systems further improve on this because of their ability to learn and improve independently; however, they are still far from perfect. In [38], the authors isolate one of the shortcomings of NMT and propose a novel approach to improve NMT by using SMT. NMT systems have a tendency to forgo accuracy for fluent translation and to improve on this, the authors introduce a hybrid approach using SMT [38]. To attain the goal of improvement, the authors implement the following steps. After the SMT and NMT models have been trained on parallel corpora, the SMT system receives the source text first as input and the translated output of the SMT is encoded. The authors then modify the NMT beam search algorithm, which gives a chance to related SMT tokens during NMT decoding [38]. After the modified beam search has been conducted, the translation sentence from the modified beam algorithm goes into the decoder, which then outputs the translated target text [38]. The intent of this improved algorithm is to increase the probability of SMT tokens that are in lower positions within the beam to be chosen in the NMT decoding step [38]. The algorithm is used in three different approaches in the paper. First, it is applied for a certain number of tokens from the start of the decoding, e.g., the first, second, or third tokens. Second, the algorithm is applied for each sentence as long as the SMT token is discovered in the beam at some part in the decoding. Lastly, it is applied until the decoding ends [38]. The authors use automatic evaluation metrics, specifically BLEU and METEOR, to draw conclusions from the experiment. They conclude that the first and second approaches provide good results in the translation quality, while the third approach, however, performs poorly. Nevertheless, they are able to successfully show that using the phrase-based SMT system can provide improvements in NMT decoding, which ultimately leads to a higher quality of translations [38]. As mentioned previously, NMT models have become a dominant approach to machine translation problems and have improved on several shortcomings of SMT over the years. Though a promising technology, NMT still faces many hurdles as its accuracy in translating several language pairs significantly depends on the availability and use of large parallel corpora [39]. For a large group of languages, however, obtaining a large parallel corpus proves to be difficult. For example, language isolates such as Basque or macaronic languages such as German-Russian [39] do not have enough data available to train an MT model efficiently.