Automatic language translation using a sequence-to-sequence LSTM model
- python
- pip
- graphviz
- notebook
- numpy
- pandas
- tensorflow
- pydot
- nltk
- scikit-learn
- matplotlib
If you have conda installed, you can create an evironment with all required packages installed by running the following commands
conda env create -f environment.yml
conda activate translationEnglish word list: https://github.com/dwyl/english-words
French word list: http://www.lexique.org/
English-to-French translation datasets:
- Sequence to Sequence Learning with Neural Networks
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
- GloVe: Global Vectors for Word Representation
- https://nlp.stanford.edu/projects/glove/
- https://google.github.io/seq2seq/
- https://keras.io/examples/nlp/lstm_seq2seq/
- https://stackabuse.com/python-for-nlp-neural-machine-translation-with-seq2seq-in-keras/
- https://machinelearningmastery.com/encoder-decoder-recurrent-neural-network-models-neural-machine-translation/
- https://machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/
- https://medium.com/@d.salvaggio/sequence-to-sequence-architectures-ad6ff4451f84
- https://towardsdatascience.com/how-to-implement-seq2seq-lstm-model-in-keras-shortcutnlp-6f355f3e5639
- https://towardsdatascience.com/neural-machine-translation-using-seq2seq-with-keras-c23540453c74