The proposed model has an average BLEU score of 45.83. In order to evaluate the efficiency of the proposed system, several automatic evaluation metrics are used, and the model output is also compared with the output from Google Translator. The corpus contains 542810 English tokens and 540924 Urdu tokens, and the proposed system is trained and tested using 70 : 30 criteria. The corpus contains sentences from English-Urdu parallel corpus, news, and sentences which are frequently used in day-to-day life. Parallel corpus sizes of around 30923 sentences are used. In this paper, we have used a neural network-based deep learning technique for English to Urdu languages.

Later on, with the advancement in computer and communication technology, there was a paradigm shift to data-driven models like statistical and neural machine translation approaches. Earlier research in this field started with the direct word-to-word replacement of source language by the target language. The main aim of machine translation is to remove the language barrier. Machine translation is an ongoing field of research from the last decades.

Translate english to urdu