translit-rnn: Automatic transliteration with LSTM

This is a tool to transliterate inconsistently romanized text. It is tested on Armenian (hy-AM). We invite everyone interested to add more languages. Instructions are below.

Read more in the corresponding blog post.

Install required packages:

pip install -r requirements.txt

Before training on the corpus we need to compute the vocabularies by the following command:

python make_vocab.py --language hy-AM

The actual training is initiated by a command like this:

python -u train.py --hdim 1024 --depth 2 --batch_size 200 --seq_len 30 --language hy-AM &> log.txt

--hdim and --depth define biLSTM parameters. --seq_len is the maximum length of a character sequence given to the network. The output will be written in log.txt.

During the training the models are saved in the model folder. The following command will run the test set through the selected model:

python -u test.py --hdim 1024 --depth 2 --model {MODEL} --language hy-AM

The above command expects that the test set contains text in the original language. The next one takes a file with romanized text and prints the transliterated text:

python -u test.py --hdim 1024 --depth 2 --model {MODEL} --language hy-AM --translit_path {FILE_NAME}

Finally, plot_loss.py command will draw the graphs for training and validation losses for the given log file. --ymax puts a limit on y axis.

python plot_loss.py --log log.txt --window 10000 --ymax 3

Preparing the data for a given language

This is what we did for Armenian. Something similar will be needed for the other not-very-different languages.

First, we prepare the corpus.

1. Download the Wikipedia dump (e.g. https://dumps.wikimedia.org/hywiki/20160901/hywiki-20160901-pages-articles.xml.bz2)
2. Extract the dump using WikiExtractor
3. Remove the remaining tags that (strings starting with ‘<’)
4. Spilt the data three parts (80% - train.txt, 10% - val.txt, 10% - test.txt) and store them in the languages/LANG_CODE/data/ folder

Next we add some language specific configuration files:

1. Populate the languages/LANG_CODE/transliteration.json file with romanization rules, like this one
2. Populate the languages/LANG_CODE/long_letters.json file with an array of the multi-symbol letters of the current language (Armenian has ու and two capitalizations of it: Ու and ՈՒ)
3. Run make_vocab.py to generate the “vocabulary”