## 5 Related Works  
This section will cover existing work in the field of MT using RBMT, SMT, and mainly NMT models. In [36], the authors propose to extend the use of a rule-based technique to simplify sentences before using an RBMT system to translate those sentences from English to Tamil. Complex sentences are made simpler using connectives such as relative pronouns, coordinating conjunctions, and subordinating conjunctions [36]. Table 2 below shows the words that were used by the system as connectives. Additionally, delimiters such as '.' and '?' are used to divide long and complex sentences into sub-sentences where the meaning of the sentence remains the same [36]. The authors chose to ignore the ',' delimiter. The authors lay out the framework as follows: first, the initial splitting of the sentences from paragraphs is performed using the delimiters. Each sentence obtained after the initial splitting is then parsed using the Stanford parser [36]. The next round of splitting is completed using the coordinating and subordinating conjunctions in each sentence. Then, the sentence is further simplified if it contains a relative pronoun [36]. To compare the system's accuracy, 200 sentences were first given to the RBMT system to translate from English to Tamil. Due to syntax and reordering, 70% of the translated sentences were incorrect. Then, the same 200 sentences were simplified using the outlined framework and given again to the RBMT system. After simplification, 57.5% of the sentences were translated correctly. The authors concluded that longer sentences that are given to the MT result in a low translation accuracy while simplifying the sentences increase the accuracy significantly when translating from English to Tamil. Although an accuracy of 100% is not possible to achieve in MT, the authors prove that the splitting and simplification technique can notably improve MT systems. In another paper titled \"Rule Based Machine Translation Combined with Statistical Post Editor for Japanese to English Patent Translation\", the authors also tackle the problem \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Metric** & **Advantages** & **Disadvantages** \\ \hline BLEU & \(\bullet\) & High correlation with human judgment of translation accuracy [32]. & \(\bullet\) & Does not account for synonyms [32]. \\ \hline NIST & \(\bullet\) & No penalty applied when translation text is shorter in length [33]. & \(\bullet\) & Does not consider synonyms, syntactic structure, or word order [33]. \\ \hline WER & \(\bullet\) & Not difficult to understand or implement [33]. & \(\bullet\) & Does not consider semantic similarity, syntactic structure, or word order of sentences [33]. \\ \hline METEOR & \(\bullet\) & Incorporates some linguistic knowledge using stem and synonym matching [33]. & \(\bullet\) & WordNet does not support most languages well except for English [33]. \\  & \(\bullet\) & Does not penalize longer sentences unlike BLEU [33]. & \(\bullet\) & Relies on WordNet so not suitable for morphologically rich languages [33]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and Disadvantages of MT Evaluation Metrics. \begin{table}
\begin{tabular}{l l} \hline \hline
**Relative Pronouns** & Who, which, whose, whom \\ \hline
**Coordinating conjunction** & For, and, not, but, or, yet, so \\ \hline
**Subordinating conjunction** & After, although, because, before, if, since, that, though, unless, where, wherever, when, whenever, whereas, while, why \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Conectives used to Simplify Sentences. of decreased accuracy due to long sentences used in machine translation. Their hypothesized solution is to use a statistical post-editor in conjunction with the RBMT system to improve accuracy. The data used by the authors were collected from the Patent Abstract of Japan, or PAJ, and the abstract of the Patent Publication Gazette (PPG) of Japan, in which the former was used as the Japanese corpus and the latter, were used as the English corpus [37].