## 2 Related Work
### Based on Handling Attention, OOV Problem, and BPE in NMT  
[PERSON] and [PERSON] (2022) developed an NMT system for English-to-Urdu pairs [11]. The parallel corpus of 30,923 was used to develop the model and the BLEU score claimed was 45.83.  
[PERSON] and [PERSON] (2022) developed an English-to-Chinese attention-based NMT system [12]. A total of 220,000 parallel corpora of Chinese-English was extracted from the 2019 International Oral English and Translation Competition. The best BLEU score claimed was 22.70 with LSTM and grammar.  
[PERSON] et al. (2022) developed a German-to-English-NMT system using gated recurrent units (GRUs) [13]. The dataset containing 400,000 German-English parallel sentences was downloaded from WMT21 (Workshop on Machine Translation). Two models were trained using GRUs with 128 and 512 units. Model 1 attained a validation accuracy of 0.653 and Model 2 attained 0.649.  
[PERSON] et al. (2021) developed the Assamese to Bengali bidirectional NMT system [14]. A parallel corpus of approximately 150,000 Assamese-Bengali sentences were used to develop two models. The BLEU score claimed 7.20 for Assamese to Bengali and 10.10 for the Bengali to Assamese NMT systems.  
[PERSON] (2021) developed a Tamil-to-Telugu bidirectional MT system using a neural network [15]. The parallel corpus of 43,143 sentences was downloaded from the WMT21. Word embedding was used with a transformer to train both models. Both models claimed a BLEU score of 4.05.  
[PERSON] et al. (2020) developed a neural machine translation system for Tamil to English [16]. A dataset comprising 236,427 parallel English-Tamil sentences were used to develop the two NMT models: one by using recurrent neural network (RNN) and the second by using the transformer architecture. The BLEU score claimed was 14.9 and 16.1 using RNN and transformer architecture.  
[PERSON] et al. (2020) developed an NMT system for Eng-lish-to-Malayalam and English-to-Tamil [17]. The pre-trained BPE and MultiBPE encodings were used to overcome the OOV problem for both systems. The BLEU score that was claimed for the English to Tamil MT system was 24.34, while the BLEU score that was claimed for the English to Malayalam MT system was 9.78.  
[PERSON] et al. (2020) developed the Chinese-to-English NMT system [18]. The transformer and DTMT architecture based on deep transition were used to develop two models. The newest of 2019 was used to train the model. The BLEU score claimed was 38.93 and 38.66 with the transformer and DTMT architecture.  
[PERSON] et al. (2019) developed an attention-based NMT system for Gujarati-to-English pairs [19]. The Hindi-English parallel corpus was combined with a Gujarati-English parallel corpus to improve the baseline model and was used in training. In this way, a multi-source translation system was developed. The model achieved a BLEU score of 9.8 with this multilingual model architecture.  
[PERSON] et al. (2019) developed an attention-based NMT system for Hindi-to-English language pairs [20]. The authors developed two models: one was the baseline model, and the other was the baseline model with the attention mechanism. The model was tested with 500 sentences and it was concluded that the BLEU score improved with the attention model compared to the BLEU score of the baseline model of the NMT.  
[PERSON] et al. (2019) developed three attention-based models: word-to-word level, character-to-word level, and syllable-to-word level [21]. The attention-based model was trained using the ALT (Asian Language Treebank) parallel corpus of 18,965 sentences and the UCSY (University of Computer Studies, Yangon) parallel corpus of 208,638 sentences. The BLEU scores claimed for all the models were 21.88, 20.71, and 26.50, respectively.  
[PERSON] et al. (2018) developed the SMT and NMT systems for English to Mizo [22]. Both the SMT and NMT systems were evaluated manually and by using automated tools. The authors concluded that for the low-resource language, the SMT model performed better than the NMT model.