## 3 The Architecture of the NMT System
### B.4. B.4. B.5.
Figure 6: Scores of BLEU and TER for the small test set. Figure 7: Scores of BLEU and TER for the medium test set. #### 5.5.4 Predictions on the Sentence Level

To check the performance of NMT models, they were tested on different sentences. Now, how each model translates a sentence is shown by predictions obtained from the model. Figure 8: Scores of BLEU and TER for the large test set. Figure 9: BLEU score of different sentences. In the input text, there was the word \"Hulgman\" (ma'krosakopa). It is a named entity, and it was correctly translated by only NMT model 4. All other models translated it incorrectly. NMT model 1 showed a \"$' sign in the output that was wrong. From all predictions given by different models, model 4 provided a more accurate translation for the input Punjabi text. ## 6 Conclusions

MT is a highlighted topic of NLP. There are various challenges in developing an accurate MT system. Out of the various challenges in the MT system are OOV words and handling MWEs. To overcome this, the NMT system was developed for the Punjabi to English system. To handle MWEs, the MWE corpora were prepared and used in the training set. Pre-trained WE for Punjabi and English was also used to handle OOV words of the target language. Different models were trained and evaluated by using human as well as automated evaluation. By using the WE, named entities, and MWE corpus, the accuracy of the Punjabi to English NMT model was improved. The limitation of the system is that it did not perform well for large sentences. Another limitation of the system is that Punjabi is a morphologically rich language and to date, the vocabulary is non-standardized, and Punjabi has various dialects that cannot be handled by this system. For example, Malawi is a dialect of Punjabi, but the system was trained on generalized Punjabi. Future Work

In future, BERT-based different models will be trained to handle MWEs. In addition to this, a large parallel corpus of Punjabi-English will be developed and models with different hyperparameters will be trained to check the effect of various parameters on the accuracy of the NMT system. In addition, work will also be carried out to develop a multilingual NMT system with the Punjabi, English, Hindi, and Dogri languages. Data Curation, K.D.G., A.K., V.G., and R.C. ; Formal Analysis, K.D.G., A.K., V.G., S.S., and B.S. ; Funding Acquisition, G.S. ; Investigation, S.S.; Methodology, K.D.G., S.S., R.C., and B.S. ; Project Administration, K.D.G., A.K., V.G., and G.S. ; Resources, B.S. and G.S. ; Software, K.D.G., A.K., V.G., and S.S.; Validation, B.S. ; Visualization, S.S. and G.S. ; Writing--Original Draft, K.D.G., A.K., V.G., B.S., R.C., and S.S.; Writing--Review and Editing, S.S., R.C., and G.S. All authors have read and agreed to the published version of the manuscript. Vellore Institute of Technology, Vellore. Not applicable. The authors declare no conflict of interest. ## References

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