An Effective Neural Machine Translation for English to Hindi Language
Saikiran Gogineni, G. Suryanarayana, Sravan Kumar Surendran
Abstract
Machine translation involves the conversion of text from one language to the other language. In the world of web, a huge number of resources are made available in English. Many of the people are not familiar with this global language. Manually transmuting them into native languages such as Hindi (Indian National language) is a tedious task. In such scenarios, automatic machine translation is an efficient approach. In our work, 8 advanced architectures have been experimented and contrasted their efficiencies. Six different Indian languages such as Hindi, Bengali, Gujarati, Malayalam, Tamil and Telugu is worked on. How BLEU varies with the usage of Word embedding technique have been clearly shown.. Encoder to decoder networks are found fine for short sentences. But if the length of the sentence exceeds 20, then attention architecture is suitable. The 4 Layer Bi-directional LSTM is a great choice in these networks to achieve higher BLEU is also observed. In our work, CFILT, UFAL, ILCC datasets have been considered and achieved a BLEU score of 21.97.