dilettacal / nmt_seq2seq

Repository containing the code to my bachelor thesis about Neural Machine Translation

Neural Machine Translation - Seq2Seq in PyTorch

This repository contains the code to my Bachelor Thesis about Neural Machine Translation.

Setup (with Conda)

Please install a conda package manager, e.g. Miniconda. This program utilizes Torchtext for data management and processing. Make sure to use the version 0.8 or lower!

Follow these instructions: 1- Create the environment from the environment.yml: conda env create -f environment.yml 2- Activate your environment, e.g. conda activate nmt-env (make sure the environment name here matches the environment in the yml file!) 3- Install spacy dependencies:

    python -m spacy download en_core_web_sm
    python -m spacy download de_core_news_sm
    python -m spacy download xx_ent_wiki_sm

Training the model

To train the model on the Europarl-Dataset, you need to first preprocess the dataset. This is possible by running the script preprocess.py. If you run the script without any arguments, the programm will download the corpus for the German language and preprocess it, by tokenizing it into words and creating the splits. All preprocessed files are stored in data/preprocessed/europarl/de/splits/30/

Then you can train the model with the script train_model.py. A list of all possible arguments can be displayed by typing: python train_model.py --help. To train the model in the best configuration achieved, run following command:

GRU: python3 train_model.py --hs 300 --emb 300 --num_layers 2 --dp 0.25 --reverse_input False --bi True --reverse True --epochs 80 --v 30000 --b 64 --train 170000 --val 1020 --test 1190 --lr 0.0002 --tok tok --tied True --rnn gru --beam 5 --attn dot

LSTM: python3 train_model.py --hs 300 --emb 300 --num_layers 2 --dp 0.25 --reverse_input False --bi True --reverse True --epochs 80 --v 30000 --b 64 --train 170000 --val 1020 --test 1190 --lr 0.0002 --tok tok --tied True --rnn lstm --beam 5--attn dot

Translate

To translate from a trained model, use the script translate.py. The script accepts following arguments:

1. --path: The path to the trained model, e.g. python train_model.py --path results/de_en/custom/lstm/2/bi/2019-08-11-10-30-31. This will start the live translation mode.
2. --file: Add this argument, if you want to translate from a file. Argument should be a valid path.
3. --beam: Add this argument to set up a beam size which is different from 5 (default value)

The beam size can be changed during the live translation mode by typing #<new_beam_size>, e.g. #10.

Note on the corpora

If you do not want to use the Europarl dataset, just run the script train_model.py by passing an empty string for the argument --corpus. This will train the model on the IWSLT-Dataset (Ted Talks) of TorchText.

Enjoy!