A Memory-Based Neural Network Model for English to Telugu Language Translation on Different Types of Sentences

In India, regional languages play an important role in government-to-public, public-to-citizen rights, weather forecasting and farming. Depending on the state the language also changes accordingly. But in the case of remote areas, the understanding level becomes complex since everything nowadays is presented in the English Language. In such conditions, the regional language manual translation consumes more time to provide services to the common people. The automatic translation of one language to another by maintaining the meaning of the given input sentence there by producing the exact meaning in the output language is carried out through Machine Translation. In this work, we proposed a Memory Based Neural Network for Translation (MBNNT) model on simple, compound and complex sentences for English to Telugu language translation. We used BLEU and WER metrics for identifying the translation quality. On applying these metrics over different type of sentences LSTM showed promising results over Statistical Machine Translation and Recurrent Neural Networks in terms of the quality and performance.