Machine-Translation-seq2seq-phrase-representation

This repo is a pytorch implementation of Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation on Multi30k dataset using De->En pair language, the algorithm achieved 20.30 Belu-score.

Setup And Inference

Code is developed under following library dependencies

python 3.7
torch 1.10.1
torchtext 0.11.1

Start with creating a virtual environment then open your terminal and follow the following steps:

git clone "https://github.com/zaghlol94/Machine-Translation-seq2seq-phrase-representation"
cd Machine-Translation-seq2seq-phrase-representation
pip install -r requirements.txt
bash download_assets.sh
cd src
python inference.py -s "your sentence"

Data-Set

Multi30k Dataset of paired with sentences in English and German. the data-set folder contains:

    ├── test_2016_flickr.de
    ├── test_2016_flickr.en
    ├── train.de
    ├── train.en
    ├── val.de
    └── val.en

There are two files of training, validation, and testing data. For each German line in train.de there is a corresponding line in train.en that represents the English translation.

Train

To train the model on custom data-set of different language pair, you could add your data files in the same structure in the data-set folder and change the train/valid files' path in config.py then follow the following steps in the command line:

cd src
python train.py

After these steps you will find model.pt in src folder, which is the weights of the seq2sq model.

Test

To test your model and print out the Belu-score, loss on the testing data, change the path of model_path, src_test and trg_test in the config.py, Then follow the following steps

cd src
python test.py

Citation

@misc{https://doi.org/10.48550/arxiv.1406.1078,
  doi = {10.48550/ARXIV.1406.1078},
  
  url = {https://arxiv.org/abs/1406.1078},
  
  author = {Cho, Kyunghyun and van Merrienboer, Bart and Gulcehre, Caglar and Bahdanau, Dzmitry and Bougares, Fethi and Schwenk, Holger and Bengio, Yoshua},
  
  keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), Neural and Evolutionary Computing (cs.NE), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation},
  
  publisher = {arXiv},
  
  year = {2014},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}