rasoolims/zero-shot-mt

Zero Shot Neural Machine Translation

Zero-shot Machine Translation

Contents

• Installing Dependencies
  • Installation using virtualenv
  • Installation using dockers
• Train Machine Translation
  • Transliterate the source text!
  • Collect raw text for languages:
  • Train a tokenizer on the concatenation of all raw text:
  • Train MT on Parallel Data

Installing Dependencies

Installation using virtualenv

Here, I use a virtual environment but Conda should be very similar.

1. Create a virtual environment with Python-3

python3 -m venv [PATH]

2. Activate the environment

source [PATH]/bin/activate

3. Clone the code

git clone https://github.com/rasoolims/zero-shot-mt
cd zero-shot-mt/src

4. Install requirements In my experiments, I used cuda 10.1 and cudnn 7. To replicate the results, please use the mentioned versions. If things do not work as expected, please use the Docker installation.

python3 -m pip install --upgrade pip
pip install -r requirements.txt

It might be the case that pyicu does not work properly. Follow its instructions to install it.

Installation using dockers

Asuuming that Docker and NVIDIA docker is installed., follow the following steps:

1. Download the repository and pretrained models

git clone https://github.com/rasoolims/zero-shot-mt

2. Build the docker in command line:

docker build dockers/gpu/ -t [docker-name] --no-cache

3. Start running the docker:

• Run this with screen since training might take a long time.

docker run --gpus all -it  [docker-name]

Train Machine Translation

Throughout this guideline, I use the small files in the sample folder. Here the Persian and English files are parallel but the Arabic text is not!

WARNING: Depending on data, the best parameters might significantly differ. It is good to try some parameter tuning for finding the best setting.

Transliterate the source text!

python3 scripts/icu_transliterate.py sample/fa.txt sample/fa.tr.txt

Collect raw text for languages:__

Then, we concatenate the three files. Note that this could be any number of files or languages more than or equal to two.

cat sample/en.txt sample/fa.tr.txt > sample/all.txt

Train a tokenizer on the concatenation of all raw text:__

Now we are ready to train a tokenizer:

python train_tokenizer.py --data sample/all.txt --vocab [vocab-size] --model sample/tok

The vocab size could be any value. Anything between 30000 to 100000 should be good. For this sample file, try 1000.

Train MT on Parallel Data

Parallel data could be gold-standard or mined. You should load pre-trained MASS models for the best performance.

1. Create binary files for training and dev dataset: For simplicity, we use the Persian and English text files as both training and development datasets by using their last 100 sentences as development data.

head -9900 sample/fa.txt > sample/train.fa
head -9900 sample/en.txt > sample/train.en
tail -100 sample/en.txt > sample/dev.en
tail -100 sample/fa.txt > sample/dev.fa
head -9900 sample/fa.tr.txt > sample/train.tr.fa
tail -100 sample/fa.tr.txt > sample/dev.tr.fa

python create_mt_batches.py --tok sample/tok/ --src sample/train.fa \
 --dst sample/train.en --srct sample/train.tr.fa\
 --output sample/fa2en.train.mt
  
python create_mt_batches.py --tok sample/tok/ --src sample/dev.fa \
 --dst sample/train.en --srct sample/dev.tr.fa  \
 --output sample/fa2en.dev.mt

If you create translation data in multiple direction, you can train multilingual translation for which we learn translation from multiple directions. Multiple data files can be separated by , in the arguments both for --train and --dev options.

2. Train machine translation:

 CUDA_VISIBLE_DEVICES=0 python3 -u train_mt.py --tok  sample/tok/ \
 --model sample/mt_model  --train sample/fa2en.train.mt \
 --capacity 600 --batch 4000   --beam 4 --step 500000 --warmup 4000 \
 --lr 0.0001  --dev sample/fa2en.dev.mt \
 --dropout 0.1 --multi

After you are done, you can use the model path sample/mt_model for translating text to English (similar to the section on using the pretrained models in our paper.

3. Translate:

CUDA_VISIBLE_DEVICES=0 python -u translate.py --tok sample/tok/ \
--model sample/mt_model --input sample/dev.fa --input2 sample/dev.tr.fa\
--output sample/dev.output.en 

Note that there is a --verbose option where it puts the input and output lines separated by |||. This is useful especially if you want to use it for back-translation (to make sure that sentence alignments are completely guaranteed), or for annotation projection in which you might need it for word alignment.