# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
### Challenges and Contributions
Especially, the BLEU score is multiplied by 100 to make it a score in a 0 to 100 interval by convention [12]. Now, a question arises in our mind: what is a good BLEU score? Usually, a BLEU score higher than 30 is considered a good score. For the interpretation of BLEU scores, a rough guideline is provided by Google (Interpretation of BLEU Scores [[https://cloud.google.com/translate/automl/docs/evaluate](https://cloud.google.com/translate/automl/docs/evaluate)]([https://cloud.google.com/translate/automl/docs/evaluate](https://cloud.google.com/translate/automl/docs/evaluate)) accessed on 1 August 2024). If the score lies between 30-40, it means that \"Understandable to good translations\". Similarly, 40-50 interprets \"High-quality translations\", 50-60 interprets \"Very high quality, adequate, and fluent translations\", and \(>\)60 interprets \"Quality often better than human\". So far, we have presented our study thoroughly. Now, for the sake of clarity, we summarize it. As such, the key aspects are as follows:  
* The first aspect is a comparison between LSTM and Transformer on English-Bengali machine translation. We find that the Transformer architecture has better accuracy even with a small training dataset (BLEU: LSTM 3.62 on validation, 0.00 on test; Transformer 27.80 on validation, 1.33 on test). Specifically, this is our baseline study to evaluate whether the Transformer can perform better in our study of English-Bengali machine translation. In our further study, we use the baseline Transformer as the basis and employ back-translation with decoding strategies as mentioned earlier and in the following aspect. * The second aspect is performing back-translation, reversing source and target languages. However, what is the novelty of doing this? Since we have a low-resource English-Bengali corpus, the model will not have a very high level of generalization ability. In this case, if we perform inference with the monolingual Bengali datasets as the training set, the Translation accuracy or quality will not be very good. Further, we would like to add that even if we train the model with a large dataset like BanglaNMT [18], the generalization ability of the model reaches a certain level. Therefore, in this case, the back-translation does not help much in augmenting machine translation. To this end, our goal is to augment the English-Bengali corpus with back-translation while incorporating decoding strategies that improve translation accuracy and quality. Since NMT is a data-driven task, we can repeat this process. Especially, we can employ this process for domain-specific translation tasks since achieving high accuracy in domain-specific machine translation is very challenging due to the lack of authentic parallel corpora, and understanding the languages by the model is a very complex task even though we may have high-resource parallel corpora. In sum, we use back-translation to boost our base Transformer model incorporated with the two decoding strategies, such as top-\(k\) random sampling and random sampling with temperature \(T\). We observe that decoding by top-\(k=+\infty\) random sampling helps improve the accuracy (BLEU 38.22, +10.42 on validation, 2.07, +0.74 on test), while sampling with a proper value of \(T\) makes the model achieve a higher score (\(T=0.5\), BLEU 35.02, +7.22 on validation, 2.35, +1.02 on test) than the first strategy (top-\(k\) random sampling).