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Machine Translation for Indian Languages Utilizing Recurrent Neural Networks and Attention

2022; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-981-19-2281-7_55

1876-1119

Sonali Sharma, Manoj Diwakar,

Multimodal Machine Learning Applications

Neural Machine Translation better known as NMT is an end-to-end approach for autonomous language translation that utilizes neural models. This is an effort to bridge the gap between the multinational and multilingual people to understand their views. The NMT systems involves models to learn directly through mapping of input–output which has proven to generate increased accuracy outputs. This technique has made remarkable accomplishments and has overcome the weakness of the conventional translations models. The paper implements the RNN, Attention based mechanism and transformer on Indian-English language pairs. So far there are no specific benchmarks for Indian languages. There are companies such as Facebook, Bing, Google whose translators supports few Indian languages. In this research work models have been trained on two set of Indian language pairs which have been retrieved from open source platform Tatoeba.