Machine Translation

Introduction

Build a deep neural network that functions as part of an end-to-end machine translation pipeline. The pipeline accepts English text as input and returns the French translation. There are three main steps:

• Preprocess - Convert text to sequence of integers.
• Models - Create models which accept a sequence of integers as input and return a probability distribution over possible translations.
• Prediction - Run the best model on English text.

Models

I have experimented with five different neural network architectures.

• Model 1 is a simple RNN
• Model 2 is an RNN with Embedding
• Model 3 is a Bidirectional RNN
• Model 4 is an Encoder-Decoder RNN
• Model 5 incorporates embedding, encoder-decoder and bidirectional RNN. It is the best performer, achieving an accuracy of 99%.

Code

• machine_translation.ipynb - The main code for this project, which contains the three steps mentioned above.
• helper.py - Contains a helper function to load data.
• project_tests.py - Tests the functions implemented in machine_translation.ipynb.

Setup

• Python 3
• NumPy
• TensorFlow 1.x
• Keras 2.x

Run

To run any notebook, use:

jupyter notebook machine_translation.ipynb

To run any script file, use:

python <script.py>