# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 4 Experiments
### Pre-Processing  
In this section, we present the pre-processing tasks of our study [92].  
* **Experimental Setup:** In our study, we conducted our experiments on the supercomputer at Macau University of Science and Technology. The setup for a personal user is: CPU: Intel Xeon E52098, GPU: NVIDIA Tesla V100.
* **Data Filtering:** Upon receiving the dataset, we first perform the data filtering. With data filtering, we prune the low-quality segments that can help optimize the translation accuracy and quality. Especially without filtering, the dataset may include misalignments, duplicates, empty segments, and other issues.
* **Tokenization/Subwording:** To train our model, we first need to build a vocabulary for the machine translation task. To this end, we usually tokenize/split sentences into words, which is called Word-based tokenization. This issue is that, in this case, our model is limited to learning a certain number of vocabulary tokens. To address this issue, subword tokenization is the preferred method over whole words. During  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **English** & **Bengali** \\ \hline \multirow{4}{*}{WikiMatrix} & Since then, the National Geographic & \\  & Society has catalogued the collection, & \\  & which appears to be & \\  & complete—22,000 objects. & \\ \hline \multirow{4}{*}{GlobalVoices} & Today is the International Mother & \\  & Language Day, an annual event in & \\  & UNESCO member states to promote & \\  & linguistic and cultural diversity and & \\  & multilingualism. & \\ \hline \multirow{4}{*}{Tanzil} & They are indeed those who bartered & \\  & away good guidance for error and & \\  & gained nothing from the deal, nor & \\  & found the right way. & \\ \hline Tatoeba & I have to go to sleep. & \\ \hline SUPara & I love my motherland. We live & \\  & in society. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parallel sentences from datasets.  
translation, if the model encounters a new word/token that resembles one in its vocabulary, it may attempt to translate it instead of labeling it as \"unknown\" or \"unk\". The most commonly used subwording approaches are byte pair encoding (BPE) and the Unigram model [93]. In our experimental analysis, we observe that the results of these two models are almost similar. Notably, we find that both models are integrated with OpenNMT-py, as we stated earlier that we conduct our analysis with OpenNMT-py. In our study, we by default use the Unigram model for subword tokenization. Notably, after translation, we have to \"desubword\" our text back employing the same subword tokenization model.
* **Data Splitting:** We use GlobalVoices, Tanzil, and Tatoeba datasets for both training and validation. We extract the first 500 pairs of sentences from each dataset to construct the validation set, then leave the rest for training. On the other hand, instead of extracting the test set from the datasets used for training and validation, we use the SUpara dataset (SUPara benchmark) for testing. After the pre-processing tasks, next comes our experimental study.