# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 5 Summary
### Limitations and Further Study  
We can further optimize the English-Bengali translation in several ways as follows:  
* For English-Bengali NMT, we can augment the limited parallel corpus by back- and forward translation. Since these methods simply need monolingual data, training models to generate monolingual text in English and Bengali will be beneficial for further study.
* We can explore the explanation for the phenomenon that the BLEU of Bengali to English translation by the Transformer is lower than that of the En to Bn translation.
* We can develop an algorithm to automatically refine the existing parallel corpus.
* We can exploit different datasets for training, validation, and testing. Compared with dividing one dataset into three parts, this method is expected to help select the model with the best generalization ability.
* We can employ our study for domain-specific translation tasks. Achieving high accuracy in a domain-specific machine translation is very challenging due to the lack of authentic parallel corpora. Notably, even if we have the high-resource parallel corpora for domain-specific translation tasks, for a model, it is a very complex task to understand the languages because of domain-specific vocabularies. Therefore, there is an urgent need to build domain-specific machine translation engines, and we plan to step into it in the future.  
Conceptualization, S.K.M., C.W., Y.C. ([PERSON]), Y.C. ([PERSON]) and Y.H.; Methodology, S.K.M., C.W., Y.C. ([PERSON]), Y.C. ([PERSON]), Y.H., H.-N.D. and H.M.D.K.; Software, C.W., S.K.M., Y.C. ([PERSON]), Y.H., Y.C. ([PERSON]) and H.M.D.K.; Validation, C.W. and S.K.M.; Formal analysis, S.K.M., C.W., Y.C. ([PERSON]), Y.H., Y.C. ([PERSON]), H.-N.D. and H.M.D.K.; Investigation, C.W. and S.K.M.; Resources, C.W. and S.K.M.; Data curation, C.W. and S.K.M.; Writing--original draft, S.K.M., C.W., Y.C. ([PERSON]) and H.M.D.K.; Writing--review & editing, S.K.M., C.W., Y.C. ([PERSON]), Y.H., Y.C. ([PERSON]), H.-N.D. and H.M.D.K.; Visualization, C.W. and S.K.M.; Supervision, S.K.M. and H.-N.D.; Project administration, S.K.M.; Funding acquisition, S.K.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by The Science and Technology Development Fund of Macao, Macao SAR, China under grant 0033/2022/ITP.  
Not applicable.  
The data presented in this study are openly available in [WikiMatrix] at [10.18653/v1/2021.eacl-main.115], reference number [89]; in [GlobalVoices] at [10.18653/v1/D19-5411], reference number [90]; in [SUPara] at [10.21227/gz0b-5p24], reference number [91]. Publicly available datasets were analyzed in this study. This data can be found here: [[https://tanzil.net/trans/](https://tanzil.net/trans/)]([https://tanzil.net/trans/](https://tanzil.net/trans/)); [[https://tatoeba.org/](https://tatoeba.org/)]([https://tatoeba.org/](https://tatoeba.org/)) (accessed on 1 August 2024).  
Authors gratefully acknowledge funding source. The authors also would like to thank the anonymous reviewers for their quality reviews and suggestions.  
The authors declare no conflicts of interest.