Neural Machine Translation (NMT) using Keras
A step by step implementation of neural machine translation using sequence to sequence algorithm in keras. Urdu as the target language and English as the source language.
This article provides a step-by-step breakdown of how to create a neural machine translator that uses Urdu as the target language and English as the source language. In keras, we’ll be utilizing Long Short Term Memory (LSTM) units.
Prerequisites:
A step by step implementation of a neural machine translation(NMT) using Teacher forcing without Attention mechanism. Implementation is using keras library with LSTM as the basic block
High-level steps for implementation of NMT involves
- Reading the data from the file containing the source and target sentences
- Cleaning the data by converting to lowercase, removing spaces, special characters, digits, and quotes
- Tag the start and end of the target sentence using START_ and _END respectively for training and inference
- Create the dictionary of unique source and target words to vector and vice-versa
- Shuffle the data for better generalization
- Split the dataset into train and test data
- Create the data; we will be using fit() to fit the data to the model
- Build the encoder using Embedding and LSTM layers
- Build the decoder using Embedding and LSTM layers and takes input from the embedding layer and the encoder states.
- Compile the model and train the model
- Make predictions from the model
Implementation
All code implemented in Google Colab Notebook which is appended below as well as code and dataset also available on GitHub repository .
Some predictions were good, some reasonable and some not right.
Code and dataset available on GitHub repository .
Reference:
Additional Information:
If you want to implement sequence to sequence with attention in PyTorch below blog will help you.
