Machine Translation with Natural Language Processing

Built a deep neural network that functions as part of an end-to-end machine translation pipeline, using Keras. The pipeline accepts English text as input and return the French translation.

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Main Steps:

1. Preprocess - Convert text to sequence of integers
2. Models - Create models which accept a sequence of integers as input and returns a probability distribution over possible translations. Experimented with different model types and techniques.
3. Prediction Run the model on English text.

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High-Level Overview of Models Implemented:

• Model 1 is a simple RNN
• Model 2 is a RNN with Embedding
• Model 3 is a Bidirectional RNN
• Model 4 is an optional Encoder-Decoder RNN