RNN-Based Machine Translation for Indian Languages
Shashi Pal Singh, Ajai Kumar, Meenal Jain · 2023
Machine translation (MT) is an important part of natural language processing (NLP). The recent advancement in deep learning (DL) has enabled us to implement the MT techniques in neural networks. Deep neural networks are powerful models that give powerful results when we train the model on a large set of data. Word embeddings play an important role in representing the syntactic and semantic relationships between the words in the form of low-dimensional dense vectors. Word2Vec or GloVe vectors can be used to initialize these word embeddings. A many-to-many encoder-decoder model helps us encode the meaning of the source sentence in a single vector to be decoded and translated into another language. In this chapter, the Recurrent Neural Network (RNN) architecture is used to implement our sequence-to-sequence model with gated RNN units to design an MT model for Indian languages like Hindi and Gujarati.