Implementing English to French Language translation using Machine Learning
A project on Sequence to Sequence Neural Networks : involves Encoders - Decoders
First implementation: I have implemented a character-level sequence-to-sequence model, processing the input character-by-character and generating the output character-by-character.
Steps of implementation -
- Turn the sentences into 3 Numpy arrays, encoder_input_data, decoder_input_data, decoder_target_data:
encoder_input_data is a 3D array of shape (num_pairs, max_english_sentence_length, num_english_characters) containing a one-hot vectorization of the English sentences.
decoder_input_data is a 3D array of shape (num_pairs, max_french_sentence_length, num_french_characters) containg a one-hot vectorization of the French sentences.
decoder_target_data is the same as decoder_input_data but offset by one timestep. decoder_target_data[:, t, :] will be the same as decoder_input_data[:, t + 1, :].
2)Train a basic LSTM-based Seq2Seq model to predict decoder_target_data given encoder_input_data and decoder_input_data. The model uses teacher forcing. In teacher forcing, basically decoder LSTM is trained to turn the target sequences into the same sequences but offset by one timestep in the future
3)Decode some sentences to check that the model is working (i.e. turn samples from encoder_input_data into corresponding samples from decoder_target_data).
Second implementation: Another implementation is a word-level model, which tends to be more common for machine translation.
