Repository for the paper Restoring Hebrew Diacritics Without a Dictionary by Elazar Gershuni and Yuval Pinter.

Demo: https://nakdimon.org/

Build the docker container:

$ docker build -t nakdimon .

Run the docker container:

$ docker run --rm --gpus all --user 1000:1000 -it nakdimon /bin/bash

The --gpus all flag is required to run the container with GPU support.

To train, test and evaluate the system, run the following commands:

> python nakdimon train --model=models/Nakdimon.h5
> python nakdimon run_test --test_set=tests/new --model=models/Nakdimon.h5
> python nakdimon results --test_set=tests/new --systems Snopi Morfix Dicta MajAllWithDicta Nakdimon

The first step trains the model and create a file named Nakdimon.h5 in the models directory. By default, the model is the one described in the paper: models/Nakdimon.h5. If the model already exists, you may skip this step.

The second step asks the Nakdimon server to predict the diacritics for the test set. You may skip this step. A folder for the results is created in the chosen test folder, with the same name as the model; in this case, tests/new/NakdimonNew. By default, the test set is the one used in the paper (tests/new); you can use tests/dicta instead. If the test results already exist, you may skip this step. If you are not sure, you can use the --skip_existing flag.

The third step calculates and prints the results (DEC, CHA, WOR and VOC metrics, as well as OOV_WOR and OOV_VOC). By default, the systems are the folders in the chosen test folder. For the Dicta test set (/tests/dicta) you should use MajAllNoDicta instead of MajAllWithDicta, otherwise the vocabulary for the Majority would include the test set itself.

> python nakdimon predict input_file.txt output_file.txt

You can use the run_test command to run the test set on other systems, such as Dicta:

> python nakdimon run_test --test_set=tests/new --system=Dicta

This will create a folder named Dicta for the results in the tests/new folder. Note that Morfix cannot be used in this manner, as its license prohibit automatic use.

You can use the --ablation flag to train different models for the ablation tests and other experiments:

> python nakdimon train --model=models/SingleLayer.h5 --ablation=SingleLayer

See the file ablation.py for the list of available ablation parameters.

• hebrew_diacritized is the training set.
• tests contains three tests sets: new, dicta and validation. Each test set has an expected folder that describes the ground truth. The results of python nakdimon run_test are stored in sibling folder, named after the model.
• models contains the trained model.
• nakdimon holds the source code.

@inproceedings{gershuni2022restoring,
  title={Restoring Hebrew Diacritics Without a Dictionary},
  author={Gershuni, Elazar and Pinter, Yuval},
  booktitle={Findings of the Association for Computational Linguistics: NAACL 2022},
  pages={1010--1018},
  year={2022}
}

Gershuni, Elazar, and Yuval Pinter. "Restoring Hebrew Diacritics Without a Dictionary." Findings of the Association for Computational Linguistics: NAACL 2022. 2022.