The architecture that we will be defining to perform machine translation is as follows:

• Take a labeled dataset where the input sentence and the corresponding translation in French is available
• Tokenize and extract words that are frequent in each of the English and French texts:
  • To identify the frequent words, we will count the frequency of each word
  • The words that constitute the top 80% of total cumulative frequency of all words are considered the frequent words
• For all the words that are not among the frequent words, replace them with an unknown (unk) symbol
• Assign an ID to each word
• Build an encoder LSTM that fetches the vector of the input text
• Pass the encoded vector through dense layer so that we extract the probabilities of decoded text at each time step
• Fit a model to minimize the loss at the output