camel_parser_dialects

CamelParser-Dialects is a state-of-the-art dependency parsing model for dialectal Arabic and Modern Standard Arabic (MSA), designed under the CATiB dependency formalism.

It is based on the biaffine attention parser architecture introduced by Dozat and Manning (2017), implemented using SuPar. The model leverages CamelBERT-MIX, a pretrained language model trained on a large and diverse Arabic corpus.

Full details are available in our paper: "Parsing Arabic Dialects Revisited: New Benchmarks, Models, and Insights"

📊 Model Variants

	Checkpoint	Training Data	MSA	EGY	GLF	AVG
	CAMeL-Lab/camelparser-dialects-MSA	CamelTB, PATB	87.3	73.0	73.3	77.9
	CAMeL-Lab/camelparser-dialects-EGY	ARZTB	79.2	83.9	68.7	77.3
	CAMeL-Lab/camelparser-dialects-GLF	CamelTB-Gumar	65.4	58.7	73.8	66.0
	CAMeL-Lab/camelparser-dialects-MSA-EGY	CamelTB, PATB, ARZTB	87.1	84.4	70.1	79.8
	CAMeL-Lab/camelparser-dialects-MSA-GLF	CamelTB, PATB, CamelTB-Gumar	87.2	74.4	81.0	80.9
	CAMeL-Lab/camelparser-dialects-EGY-GLF	ARZTB, CamelTB-Gumar	80.0	83.8	79.4	81.1
	CAMeL-Lab/camelparser-dialects-MSA-EGY-GLF	CamelTB, PATB, ARZTB, CamelTB-Gumar	87.2	84.2	80.3	83.9

• LAS (Labeled Attachment Score) on TEST
• The recommended checkpoint is the all-variety model (MSA-EGY-GLF), which provides the best overall cross-dialect performance.
• Model weights are compatible with CamelParser2.0 and SuPar libarary. Please refer to these libraries to run these model checkpoints. Further documentattion will be provided shortly in this repository.

📚Data

The models are trained on combinations of the following treebanks:

• CamelTB (MSA): camel_treebank_1.1.zip
• PATB (Penn Arabic Treebank): LDC2010T13, LDC2011T09, LDC2010T08
• ARZTB (Egyptian Arabic Treebank): LDC2018T23
• CamelTB-Gumar (Gulf Arabic): CamelTB-Gumar.1.0.zip

The preprocesessed data can be extracted using muddler. Once installed with pip install muddler, extract muddled files provided under data/ directory with the following files.

• CamelTB (MSA):

  1. Download camel_treebank_1.1.zip from:

     • https://sites.google.com/nyu.edu/camel-treebank/resources

  2. Run the following command to unlock the muddled file.

     muddler unmuddle -s camel_treebank_1.1.zip -m data/CamelTB.zip.muddle data/CamelTB.zip

  3. Unzip the file with unzip data/CamelTB.zip -f data

• PATB (Penn Arabic Treebank):

  1. Download the following files from the following LDC releases:

     • atb1_v4_1_LDC2010T13.tgz: https://catalog.ldc.upenn.edu/LDC2010T13
     • atb_2_3.1_LDC2011T09.tgz: https://catalog.ldc.upenn.edu/LDC2011T09
     • atb3_v3_2_LDC2010T08.tgz: https://catalog.ldc.upenn.edu/LDC2010T08

  2. Place them in a directory, e.g., ldc_files/

  3. Run the following command to unlock the muddled file.

     muddler unmuddle -s ldc_files -m data/PATB.zip.muddle data/PATB.zip

  4. Unzip the file with unzip data/PATB.zip -d data

• ARZTB (Egyptian Arabic Treebank):

  1. Download bolt_arz-df_LDC2018T23.tgz from:

     • https://catalog.ldc.upenn.edu/LDC2018T23

  2. Run the following command to unlock the muddled file.

     muddler unmuddle -s bolt_arz-df_LDC2018T23.tgz -m data/arz_data.zip.muddle data/arz_data.zip

  3. Unzip the file with unzip data/arz_data.zip -d data

• CamelTB-Gumar (Gulf Arabic):

  1. Download CamelTB-Gumar.1.0.zip from:

     • https://forms.gle/54WSUt7Z9m9vk6p69

  2. Run the following command to unlock the muddled file.

     muddler unmuddle -s CamelTB-Gumar.1.0.zip -m data/CamelTB-Gumar_data.zip.muddle data/CamelTB-Gumar_data.zip

  3. Unzip the file with unzip data/CamelTB-Gumar_data.zip -data

📖 Citation

If you use this model, please cite:

@inproceedings{Elshabrawy:2026:camelparser-dialects,
    title = "{Parsing Arabic Dialects Revisited: New Benchmarks, Models, and Insights}",
    author = {Ahmed Elshabrawy and
              Go Inoue and
              Muhammed AbuOdeh and
              Nizar Habash} ,
    booktitle = {Proceedings of The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT)},
    year = "2026",
    address = "Palma, Spain"
}