Neural Machine Translation: English to Hindi
Sahinur Rahman Laskar,Abinash Dutta,Partha Pakray,Sivaji Bandyopadhyay
TLDR
Two different NMT systems are carried out, namely, NMT-1 relies on the Long Short Term Memory (LSTM) based attention model and N MT-2 depends on the transformer model in the context of English to Hindi translation, and results show better performance than existing N MT systems.
Abstract
Machine Translation (MT) attempts to minimize the communication gap among people from various linguistic backgrounds. Automatic translation between pair of different natural languages is the task of MT mechanism, wherein Neural Machine Translation (NMT) attract attention because it offers reasonable translation accuracy in case of the context analysis and fluent translation. In this paper, two different NMT systems are carried out, namely, NMT-1 relies on the Long Short Term Memory (LSTM) based attention model and NMT-2 depends on the transformer model in the context of English to Hindi translation. System results are evaluated using Bilingual Evaluation Understudy (BLEU) metric. The average BLEU scores of NMT-1 system are 35.89 (Test-Set-1), 19.91 (Test-Set-2) and NMT-2 system are 34.42 (Test-Set-1), 24.74 (Test-Set-2) respectively. The results show better performance than existing NMT systems.